{"title":"Pattern recognition Books","description":"","products":[{"product_id":"biometrics-9780198809104","title":"Biometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWe live in a society which is increasingly interconnected, in which communication between individuals is mostly mediated via some electronic platform, and transactions are often carried out remotely. In such a world, traditional notions of trust and confidence in the identity of those with whom we are interacting, taken for granted in the past, can be much less reliable. Biometrics - the scientific discipline of identifying individuals by means of the measurement of unique personal attributes - provides a reliable means of establishing or confirming an individual''s identity. These attributes include facial appearance, fingerprints, iris patterning, the voice, the way we write, or even the way we walk. The new technologies of biometrics have a wide range of practical applications, from securing mobile phones and laptops to establishing identity in bank transactions, travel documents, and national identity cards. This Very Short Introduction considers the capabilities of biometrics-based identity checking, from first principles to the practicalities of using different types of identification data. Michael Fairhurst looks at the basic techniques in use today, ongoing developments in system design, and emerging technologies, all aimed at improving precision in identification, and providing solutions to an increasingly wide range of practical problems. Considering how they may continue to develop in the future, Fairhurst explores the benefits and limitations of these pervasive and powerful technologies, and how they can effectively support our increasingly interconnected society.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: Are you who you say you are?2: Biometrics: where should I start?3: Making biometrics work4: Enhancing biometric processing5: An introduction to predictive biometrics6: Where are we going?Further readingIndex","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732791341399,"sku":"9780198809104","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"introduction-to-biometrics-9780387773254","title":"Introduction to Biometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48733726310743,"sku":"9780387773254","price":59.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387773254.jpg?v=1720001398"},{"product_id":"explainable-ai-for-practitioners-9781098119133","title":"Explainable AI for Practitioners","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48738226798935,"sku":"9781098119133","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098119133.jpg?v=1723811837"},{"product_id":"natural-language-processing-9781108420211","title":"Natural Language Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'An amazingly compact, and at the same time comprehensive, introduction and reference to natural language processing (NLP). It describes the NLP basics, then employs this knowledge to solve typical NLP problems. It achieves very high coverage of NLP through a clever abstraction to typical high-level tasks, such as sequence labelling. Finally, it explains the topics in deep learning. The book captivates through its simple elegance, depth, and accessibility to a wide range of readers from undergrads to experienced researchers.' Iryna Gurevych, Technical University of Darmstadt, Germany\u003cbr\u003e'An excellent introduction to the field of natural language processing including recent advances in deep learning. By organising the material in terms of machine learning techniques - instead of the more traditional division by linguistic levels or applications - the authors are able to discuss different topics within a single coherent framework, with a gradual progression from basic notions to more complex material.' Joakim Nivre, Uppsala University\u003cbr\u003e'The book is a valuable tool for both beginning and advanced researchers in the field.' Catalin Stoean, zbMATH\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Basics: 1. Introduction; 2. Counting relative frequencies; 3. Feature vectors; 4. Discriminative linear classifiers; 5. A perspective from information theory; 6. Hidden variables; Part II. Structures: 7. Generative sequence labelling; 8. Discriminative sequence labelling; 9. Sequence segmentation; 10. Predicting tree structures; 11. Transition-based methods for structured prediction; 12. Bayesian models; Part III. Deep Learning: 13. Neural network; 14. Representation learning; 15. Neural structured prediction; 16. Working with two texts; 17. Pre-training and transfer learning; 18. Deep latent variable models; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738286666071,"sku":"9781108420211","price":55.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108420211.jpg?v=1723811892"},{"product_id":"machine-learning-refined-9781108480727","title":"Machine Learning Refined","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego\u003cbr\u003e'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London\u003cbr\u003e'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto\u003cbr\u003e'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan\u003cbr\u003e'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory\u003cbr\u003e'With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.' politcommerce.com\u003cbr\u003e'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljević, zbMATH\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738306556247,"sku":"9781108480727","price":55.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108480727.jpg?v=1723811911"},{"product_id":"pattern-recognition-and-machine-learning-9781493938438","title":"Pattern Recognition and Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews:\u003c\/p\u003e \u003cp\u003e\"This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group.\" \u003cem\u003eJohn Maindonald for the Journal of Statistical Software\u003c\/em\u003e\u003c\/p\u003e \u003cp\u003e\"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields.\" (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007)\u003c\/p\u003e \u003cp\u003e\"This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study.\" (W. R. Howard, Kybernetes, Vol. 36 (2), 2007)\u003c\/p\u003e \u003cp\u003e\"Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals.\" (C. Tappert, CHOICE, Vol. 44 (9), May, 2007)\u003c\/p\u003e \u003cp\u003e\"The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … .\" (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007)\u003c\/p\u003e \u003cp\u003e\"This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value.\" (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007)\u003c\/p\u003e \u003cp\u003e\"Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record.\" (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008)\u003c\/p\u003e \u003cp\u003e\"This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … .\" (L. State, ACM Computing Reviews, October, 2008)\u003c\/p\u003e \u003cp\u003e\"Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts.\" (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48739724493143,"sku":"9781493938438","price":64.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"an-intuitive-exploration-of-artificial-intelligence-theory-and-applications-of-deep-learning-9783030686260","title":"An Intuitive Exploration of Artificial","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.\u003cbr\u003e\u003ci\u003eAn Intuitive Exploration of Artificial Intelligence\u003c\/i\u003e offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003eThe book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI,  practitioners and researchers will equally benefit from the book.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I, Foundations.- AI Sculpture.- Make Me Learn.- Images and Sequences.- Why AI Works.- Learning to Sculpt.- Unleashing the Power of Generation.- The Road Most Rewarded.- The Classical World.- Part II, Applications.- To See is to Believe.- Read, Read, Read.- Lend Me Your Ear.- Create Your Shire and Rivendell.- Math to Code to Petaflops.- AI and Business.- Part III, Road Ahead.- Keep Marching on.- Benevolent AI for All.- Am I Looking at Myself?.- App. A, Solutions.- Further Reading.- Acronyms.- Glossary.- References.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743045366103,"sku":"9783030686260","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030686260.jpg?v=1720063870"},{"product_id":"fundamentals-of-music-processing-using-python-and-jupyter-notebooks-9783030698072","title":"Fundamentals of Music Processing: Using Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval (MIR). Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, signal processing, computer science, digital humanities, and musicology.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform—concepts used throughout the book. Each of the subsequent chapters starts with a general description of a concrete music processing task and then discusses—in a mathematically rigorous way—essential techniques and algorithms applicable to a wide range of analysis, classification, and retrieval problems. By mixing theory and practice, the book’s goal is to offer detailed technological insights and a deep understanding of music processing applications.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAs a substantial extension, the textbook’s second edition introduces the FMP (fundamentals of music processing) notebooks, which provide additional audio-visual material and Python code examples that implement all computational approaches step by step. Using Jupyter notebooks and open-source web applications, the FMP notebooks yield an interactive framework that allows students to experiment with their music examples, explore the effect of parameter settings, and understand the computed results by suitable visualizations and sonifications. The FMP notebooks are available from the author’s institutional web page at the International Audio Laboratories Erlangen.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Music Representations.- 2. Fourier Analysis of Signals.- 3. Music Synchronization.- 4. Music Structure Analysis.- 5. Chord Recognition.- 6. Tempo and Beat Tracking.- 7. Content-Based Audio Retrieval.- 8. Musically Informed Audio Decomposition.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743045726551,"sku":"9783030698072","price":61.74,"currency_code":"GBP","in_stock":true}]},{"product_id":"handbook-of-digital-face-manipulation-and-detection-from-deepfakes-to-morphing-attacks-9783030876661","title":"Handbook of Digital Face Manipulation and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePart I - Introduction:\u003c\/b\u003e 1. Digital Face Manipulation: An Introduction.- 2. Face Manipulation in Biometric Systems.- 3. Face Manipulation in Media Forensics.- \u003cb\u003ePart II - Face Manipulation Detection Methods: \u003c\/b\u003e4. DeepFakes Manipulation.- 5. DeepFakes Detection.- 6. Attacking Face Recognition Systems with DeepFakes.- 7. Vulnerability of Face Recognition Systems to Morphing Attacks.- 8. Face Morphing Attack Detection.- 9. Face Synthesis Detection.- 10. Expression Swap Detection.- 11. Audio- and Text-to-Video Detection.- 12. Detection of Facial Retouching.- 13. Face De-Identification Detection.- \u003cb\u003ePart III - Further Topics:\u003c\/b\u003e 14. All-in-One Face Manipulation Detection: Generalization Analysis.- 15. Reversion of Face Manipulation.- 16. 3D Face Manipulation Detection.- 17. Improving Face Recognition with Face Image Manipulation.- 18. Impact of Post-Processing on Face Manipulation Detection.- 19. Societal and Legal Aspects of Face Manipulation.- 20. Face Manipulation for Privacy Protection.- 21. Privacy-preserving Face Manipulation Detection.- 22. Face Manipulation in Operational Systems.- \u003cb\u003ePart IV - Open Issues, Trends, and Challenges: \u003c\/b\u003e23. All: Future trends in face Manipulation and Fake Detection.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743055917399,"sku":"9783030876661","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030876661.jpg?v=1720063916"},{"product_id":"computational-intelligence-in-software-modeling-9783110705430","title":"Computational Intelligence in Software Modeling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eResearchers, academicians and professionals expone in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48743090159959,"sku":"9783110705430","price":101.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110705430.jpg?v=1720064066"},{"product_id":"the-data-science-design-manual-9783319554433","title":"The Data Science Design Manual","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis engaging and clearly written textbook\/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003ci\u003eThe Data Science Design Manual\u003c\/i\u003e\u003c\/b\u003e is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.\u003c\/p\u003e  This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAdditional learning tools:\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eContains “War Stories,” offering perspectives on how data science applies in the real world\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eIncludes “Homework Problems,” providing a wide range of exercises and projects for self-study\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides a complete set of lecture slides and online video lectures at www.data-manual.com\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eRecommends exciting “Kaggle Challenges” from the online platform Kaggle\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eHighlights “False Starts,” revealing the subtle reasons why certain approaches fail\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eOffers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e“The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018)\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e  ​\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eWhat is Data Science?\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eMathematical Preliminaries\u003c\/p\u003e  \u003cp\u003eData Munging\u003c\/p\u003e  \u003cp\u003eScores and Rankings\u003c\/p\u003e  \u003cp\u003eStatistical Analysis\u003c\/p\u003e  \u003cp\u003eVisualizing Data\u003c\/p\u003e  \u003cp\u003eMathematical Models\u003c\/p\u003e  \u003cp\u003eLinear Algebra\u003c\/p\u003e  \u003cp\u003eLinear and Logistic Regression\u003c\/p\u003e  \u003cp\u003eDistance and Network Methods\u003c\/p\u003e  \u003cp\u003eMachine Learning\u003c\/p\u003e  \u003cp\u003eBig Data: Achieving Scale\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743098220887,"sku":"9783319554433","price":45.55,"currency_code":"GBP","in_stock":true}]},{"product_id":"human-and-machine-learning-visible-explainable-trustworthy-and-transparent-9783319904024","title":"Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent","description":"\u003cp\u003eWith an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.\u003c\/p\u003e  \u003cp\u003eThis is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.\u003c\/p\u003e  \u003cp\u003eThis book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.\u003c\/p\u003e\u003cbr\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743108903255,"sku":"9783319904023","price":80.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319904023.jpg?v=1720064150"},{"product_id":"man-machine-speech-communication-14th-national-conference-ncmmsc-2017-lianyungang-china-october-11-13-2017-revised-selected-papers-9789811081101","title":"Man-Machine Speech Communication: 14th National","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book constitutes the refereed proceedings of the 14th National Conference on Man-Machine Speech Communication, NCMMSC 2017, held in Lianyungang, China, in October 2017. \u003c\/p\u003e  \u003cp\u003eThe 13 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers address issues such as challenging issues in speech recognition and enhancement, speaker and language recognition, speech synthesis, corpus and phonetic in speech technology, speech generation, speech analyzing and modelling, speech processing of ethnic minorities, speech emotion recognition and audio signal processing.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChallenging issues in speech recognition and enhancement.- Speaker and language recognition, speech synthesis.- Corpus and phonetic in speech technology.- Speech generation, speech analyzing and modelling.- Speech processing of ethnic minorities.- Speech emotion recognition.- Audio signal processing.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743275004247,"sku":"9789811081101","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"quick-start-guide-to-large-language-models-9780138199197","title":"Quick Start Guide to Large Language Models","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eSinan Ozdemir\u003c\/strong\u003e is currently the founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"Ozdemir's book cuts through the noise to help readers understand where the LLM revolution has come from--and where it is going. Ozdemir breaks down complex topics into practical explanations and easy to follow code examples.\"\u003cbr\u003e--\u003cstrong\u003eShelia Gulati\u003c\/strong\u003e, former GM at Microsoft and current Managing Director of Tola Capital\u003c\/p\u003e \u003cp\u003e\"When it comes to building Large Language Models (LLMs), it can be a daunting task to find comprehensive resources that cover all the essential aspects. However, my search for such a resource recently came to an end when I discovered this book.\u003c\/p\u003e \u003cp\u003e\"One of the stand-out features of Sinan is his ability to present complex concepts in a straightforward manner. The author has done an outstanding job of breaking down intricate ideas and algorithms, ensuring that readers can grasp them without feeling overwhelmed. Each topic is carefully explained, building upon examples that serve as steppingstones for better understanding. This approach greatly enhances the learning experience, making even the most intricate aspects of LLM development accessible to readers of varying skill levels.\u003c\/p\u003e \u003cp\u003e\"Another strength of this book is the abundance of code resources. The inclusion of practical examples and code snippets is a game-changer for anyone who wants to experiment and apply the concepts they learn. These code resources provide readers with hands-on experience, allowing them to test and refine their understanding. This is an invaluable asset, as it fosters a deeper comprehension of the material and enables readers to truly engage with the content.\u003c\/p\u003e \u003cp\u003e\"In conclusion, this book is a rare find for anyone interested in building LLMs. Its exceptional quality of explanation, clear and concise writing style, abundant code resources, and comprehensive coverage of all essential aspects make it an indispensable resource. Whether you are a beginner or an experienced practitioner, this book will undoubtedly elevate your understanding and practical skills in LLM development. I highly recommend Quick Start Guide to Large Language Models to anyone looking to embark on the exciting journey of building LLM applications.\"\u003cbr\u003e--\u003cstrong\u003ePedro Marcelino\u003c\/strong\u003e, Machine Learning Engineer, Co-Founder and CEO @overfit.study\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eForeword xv\u003c\/em\u003e\u003cbr\u003e\u003cem\u003ePreface xvii\u003c\/em\u003e\u003cbr\u003e\u003cem\u003eAcknowledgments xxi\u003c\/em\u003e\u003cbr\u003e\u003cem\u003eAbout the Author xxiii\u003c\/em\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart I: Introduction to Large Language Models 1\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 1: Overview of Large Language Models 3\u003c\/strong\u003e\u003cbr\u003eWhat Are Large Language Models? 4\u003cbr\u003ePopular Modern LLMs 20\u003cbr\u003eDomain-Specific LLMs 22\u003cbr\u003eApplications of LLMs 23\u003cbr\u003eSummary 29\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 2: Semantic Search with LLMs 31\u003c\/strong\u003e\u003cbr\u003eIntroduction 31\u003cbr\u003eThe Task 32\u003cbr\u003eSolution Overview 34\u003cbr\u003eThe Components 35\u003cbr\u003ePutting It All Together 51\u003cbr\u003eThe Cost of Closed-Source Components 54\u003cbr\u003eSummary 55\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 3: First Steps with Prompt Engineering 57\u003c\/strong\u003e\u003cbr\u003eIntroduction 57\u003cbr\u003ePrompt Engineering 57\u003cbr\u003eWorking with Prompts Across Models 65\u003cbr\u003eBuilding a Q\/A Bot with ChatGPT 69\u003cbr\u003eSummary 74\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart II: Getting the Most Out of LLMs 75\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 4: Optimizing LLMs with Customized Fine-Tuning 77\u003c\/strong\u003e\u003cbr\u003eIntroduction 77\u003cbr\u003eTransfer Learning and Fine-Tuning: A Primer 78\u003cbr\u003eA Look at the OpenAI Fine-Tuning API 82\u003cbr\u003ePreparing Custom Examples with the OpenAI CLI 84\u003cbr\u003eSetting Up the OpenAI CLI 87\u003cbr\u003eOur First Fine-Tuned LLM 88\u003cbr\u003eCase Study: Amazon Review Category Classification 93\u003cbr\u003eSummary 95\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 5: Advanced Prompt Engineering 97\u003c\/strong\u003e\u003cbr\u003eIntroduction 97\u003cbr\u003ePrompt Injection Attacks 97\u003cbr\u003eInput\/Output Validation 99\u003cbr\u003eBatch Prompting 103\u003cbr\u003ePrompt Chaining 104\u003cbr\u003eChain-of-Thought Prompting 111\u003cbr\u003eRevisiting Few-Shot Learning 113\u003cbr\u003eTesting and Iterative Prompt Development 123\u003cbr\u003eSummary 124\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 6: Customizing Embeddings and Model Architectures 125\u003c\/strong\u003e\u003cbr\u003eIntroduction 125\u003cbr\u003eCase Study: Building a Recommendation System 126\u003cbr\u003eSummary 144\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart III: Advanced LLM Usage 145\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 7: Moving Beyond Foundation Models 147\u003c\/strong\u003e\u003cbr\u003eIntroduction 147\u003cbr\u003eCase Study: Visual Q\/A 147\u003cbr\u003eCase Study: Reinforcement Learning from Feedback 163\u003cbr\u003eSummary 173\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 8: Advanced Open-Source LLM Fine-Tuning 175\u003c\/strong\u003e\u003cbr\u003eIntroduction 175\u003cbr\u003eExample: Anime Genre Multilabel Classification with BERT 176\u003cbr\u003eExample: LaTeX Generation with GPT2 189\u003cbr\u003eSinan's Attempt at Wise Yet Engaging Responses: SAWYER 193\u003cbr\u003eThe Ever-Changing World of Fine-Tuning 206\u003cbr\u003eSummary 207\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eChapter 9: Moving LLMs into Production 209\u003c\/strong\u003e\u003cbr\u003eIntroduction 209\u003cbr\u003eDeploying Closed-Source LLMs to Production 209\u003cbr\u003eDeploying Open-Source LLMs to Production 210\u003cbr\u003eSummary 225\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003ePart IV: Appendices 227\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eAppendix A: LLM FAQs 229\u003cbr\u003eAppendix B: LLM Glossary 233\u003cbr\u003eAppendix C: LLM Application Archetypes 239\u003c\/p\u003e \u003cp\u003e\u003cem\u003eIndex 243\u003c\/em\u003e\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864177684823,"sku":"9780138199197","price":34.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780138199197.jpg?v=1722270756"},{"product_id":"design-patterns-9780201633610","title":"Design Patterns","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDr. Erich Gamma \u003c\/b\u003eis technical director at the Software Technology Center of Object Technology International in Zurich, Switzerland. Dr. Richard Helm is a member of the Object Technology Practice Group in the IBM Consulting Group in Sydney, Australia. Dr. Ralph Johnson is a faculty member at the University of Illinois at Urbana-Champaign's Computer Science Department. \u003c\/p\u003e \u003cp\u003e\u003cb\u003eJohn Vlissides\u003c\/b\u003e is a member of the research staff at the IBM T. J. Watson Research Center in Hawthorne, New York. He has practiced object-oriented technology for more than a decade as a designer, implementer, researcher, lecturer, and consultant. In addition to co-authoring \u003cb\u003eDesign Patterns: Elements of Reusable Object-Oriented Software\u003c\/b\u003e, he is co-editor of the book \u003cb\u003ePattern Languages of Program Design 2\u003c\/b\u003e (both from Addison-Wesley). He and the other co-authors of \u003cb\u003eDesign Patterns\u003c\/b\u003e are recipients of the \u003ci\u003e1998 Dr. Dobb's Journal\u003c\/i\u003e Excellence in Programming Award.\u003c\/p\u003e \u003cbr\u003e \u003cbr\u003e 020163\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e1. Introduction. \u003c\/li\u003e\n\u003cli\u003e2. A Case Study: Designing a Document Editor. \u003c\/li\u003e\n\u003cli\u003e3. Creational Patterns. \u003c\/li\u003e\n\u003cli\u003e4. Structural Pattern. \u003c\/li\u003e\n\u003cli\u003e5. Behavioral Patterns. \u003c\/li\u003e\n\u003cli\u003e6. Conclusion. \u003c\/li\u003e\n\u003cli\u003eAppendix A: Glossary. \u003c\/li\u003e\n\u003cli\u003eAppendix B: Guide to Notation. \u003c\/li\u003e\n\u003cli\u003eAppendix C: Foundation Classes. \u003c\/li\u003e\n\u003cli\u003eBibliography. \u003c\/li\u003e\n\u003cli\u003eIndex. \u003cbr\u003e \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864224346455,"sku":"9780201633610","price":44.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780201633610.jpg?v=1722270971"},{"product_id":"foundations-of-data-science-9781108485067","title":"Foundations of Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This beautifully written text is a scholarly journey through the mathematical and algorithmic foundations of data science. Rigorous but accessible, and with many exercises, it will be a valuable resource for advanced undergraduate and graduate classes.' Peter Bartlett, University of California, Berkeley\u003cbr\u003e'The rise of the Internet, digital media, and social networks has brought us to the world of data, with vast sources from every corner of society. Data Science - aiming to understand and discover the essences that underlie the complex, multifaceted, and high-dimensional data - has truly become a 'universal discipline', with its multidisciplinary roots, interdisciplinary presence, and societal relevance. This timely and comprehensive book presents - by bringing together from diverse fields of computing - a full spectrum of mathematical, statistical, and algorithmic materials fundamental to data analysis, machine learning, and network modeling. Foundations of Data Science offers an effective roadmap to approach this fascinating discipline and engages more advanced readers with rigorous mathematical\/algorithmic theory.' Shang-Hua Teng, University of Southern California\u003cbr\u003e'A lucid account of mathematical ideas that underlie today's data analysis and machine learning methods. I learnt a lot from it, and I am sure it will become an invaluable reference for many students, researchers and faculty around the world.' Sanjeev Arora, Princeton University, New Jersey\u003cbr\u003e'It provides a very broad overview of the foundations of data science that should be accessible to well-prepared students with backgrounds in computer science, linear algebra, and probability theory … These are all important topics in the theory of machine learning and it is refreshing to see them introduced together in a textbook at this level.' Brian Borchers, MAA Reviews\u003cbr\u003e'One plausible measure of [Foundations of Data Science's] impact is the book's own citation metrics. Semantic Scholar (https:\/\/www.semanticscholar.org) reports 81 citations with 42 citations related to background or methods; [Foundations of Data Science] appears to be on course to becoming influential.' M. Mounts, Choice\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; 2. High-dimensional space; 3. Best-fit subspaces and Singular Value Decomposition (SVD); 4. Random walks and Markov chains; 5. Machine learning; 6. Algorithms for massive data problems: streaming, sketching, and sampling; 7. Clustering; 8. Random graphs; 9. Topic models, non-negative matrix factorization, hidden Markov models, and graphical models; 10. Other topics; 11. Wavelets; 12. Appendix.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48866356003159,"sku":"9781108485067","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108485067.jpg?v=1722278261"},{"product_id":"the-science-of-deep-learning-9781108835084","title":"The Science of Deep Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notatio\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'In the avalanche of books on Deep Learning, this one stands out. Iddo Drori has mastered reinforcement learning - in its technical meaning and in his successful, commonsense approach to teaching and understanding.' Gilbert Strang, Massachusetts Institute of Technology\u003cbr\u003e'This book covers an impressive breadth of foundational concepts and algorithms behind modern deep learning. By reading this book, readers will quickly but thoroughly learn and appreciate foundations and advances of modern deep learning.' Kyunghyun Cho, New York University\u003cbr\u003e'This book offers a fascinating tour of the field of deep learning, which in only ten years has come to revolutionize almost every area of computing. Drori provides concise descriptions of many of the most important developments, combining unified mathematical notation and ample figures to form an essential resource for students and practitioners alike.' Jonathan Ventura, Cal Poly\u003cbr\u003e'Drori's textbook goes under the hood of deep learning, covering a broad swath of modern techniques in optimization that are useful for efficiently training neural networks. The book also covers regularization methods to avoid overfitting, a common issue when working with deep learning models. Overall, this is an excellent textbook for students and practitioners who want to gain a deeper understanding of deep learning.' Madeleine Udell, Stanford University\u003cbr\u003e'This textbook provides an excellent introduction to contemporary methods and models in deep learning. I expect this book to become a key resource in data science education for students and researchers.' Nakul Verma, Columbia University\u003cbr\u003e'This new book by Professor Drori brings fresh insights from his experience teaching thousands of students at Columbia, MIT, and NYU during the past several years. The book is a unique resource and opportunity for educators and researchers worldwide to build on his highly successful deep learning course.' Claudio Silva, New York University\u003cbr\u003e'Drori's book covers deep learning, from fundamentals to applications. The fundamentals are covered with clear figures and examples, making the underlying algorithms easy to understand for non-specialists. The multidisciplinary applications are thoughtfully selected to illustrate the broad applications of deep neural networks to specialized domains while highlighting the common themes and architectures between them.' Tonio Buonassisi, Professor of Mechanical Engineering, Massachusetts Institute of Technology\u003cbr\u003e'Drori's textbook makes the learning curve for deep learning a whole lot easier to climb. It follows a rigid scientific narrative, accompanied by a trove of code examples and visualizations. These enable a truly multi-modal approach to learning that will allow many students to understand the material better and sets them on a path of exploration.' Joaquin Vanschoren, Assistant Professor of Machine Learning, Eindhoven University of Technology\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Notation; Part I. Foundations: 1. Introduction; 2. Forward and backpropagation; 3. Optimization; 4. Regularization; Part II. Architectures: 5. Convolutional neural networks; 6. Sequence models; 7. Graph neural networks; 8. Transformers; Part III. Generative Models: 9. Generative adversarial networks; 10. Variational autoencoders; Part IV. Reinforcement Learning: 11. Reinforcement learning; 12. Deep reinforcement learning; Part V. Applications: 13. Applications; Appendices; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48866361180503,"sku":"9781108835084","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108835084.jpg?v=1722278282"},{"product_id":"face-recognition-methods-applications-technology-9781619426634","title":"Face Recognition: Methods, Applications \u0026","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886935421271,"sku":"9781619426634","price":149.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781619426634.jpg?v=1722542243"},{"product_id":"computational-intelligence-for-managing-pandemics-9783110700206","title":"Computational Intelligence for Managing Pandemics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889047384407,"sku":"9783110700206","price":96.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110700206.jpg?v=1722552433"},{"product_id":"artificial-intelligence-of-things-in-smart-environments-applications-in-transportation-and-logistics-9783110755336","title":"Artificial Intelligence of Things in Smart","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book focuses on the use of AI\/ML-based techniques to solve issues related to IoT-based environments, as well as their applications. It addresses, among others, signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user\/application behavior prediction, software-defi ned networking, congestion control, communication network optimization, security, and anomaly detection. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889049710935,"sku":"9783110755336","price":84.38,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110755336.jpg?v=1722552443"},{"product_id":"deep-learning-for-cognitive-computing-systems-technological-advancements-and-applications-9783110750508","title":"Deep Learning for Cognitive Computing Systems:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eCognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889049809239,"sku":"9783110750508","price":100.88,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110750508.jpg?v=1722552444"},{"product_id":"artificial-intelligence-for-virtual-reality-9783110713749","title":"Artificial Intelligence for Virtual Reality","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book explores the possible applications of Artificial Intelligence in Virtual environments. These were previously mainly associated with gaming, but have largely extended their area of application, and are nowadays used for promoting collaboration in work environments, for training purposes, for management of anxiety and pain, etc.. The development of Artificial Intelligence has given new dimensions to the research in this field.","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":49372695724375,"sku":"9783110713749","price":117.8,"currency_code":"GBP","in_stock":true}]},{"product_id":"statistical-pattern-recognition-9780470682272","title":"Statistical Pattern Recognition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.  \u003cp\u003eThis third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illust\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e“In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 December 2012)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface xix\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eNotation xxiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Statistical Pattern Recognition 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Statistical Pattern Recognition 1\u003c\/p\u003e \u003cp\u003e1.1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.2 The Basic Model 2\u003c\/p\u003e \u003cp\u003e1.2 Stages in a Pattern Recognition Problem 4\u003c\/p\u003e \u003cp\u003e1.3 Issues 6\u003c\/p\u003e \u003cp\u003e1.4 Approaches to Statistical Pattern Recognition 7\u003c\/p\u003e \u003cp\u003e1.5 Elementary Decision Theory 8\u003c\/p\u003e \u003cp\u003e1.5.1 Bayes’ Decision Rule for Minimum Error 8\u003c\/p\u003e \u003cp\u003e1.5.2 Bayes’ Decision Rule for Minimum Error – Reject Option 12\u003c\/p\u003e \u003cp\u003e1.5.3 Bayes’ Decision Rule for Minimum Risk 13\u003c\/p\u003e \u003cp\u003e1.5.4 Bayes’ Decision Rule for Minimum Risk – Reject Option 15\u003c\/p\u003e \u003cp\u003e1.5.5 Neyman–Pearson Decision Rule 15\u003c\/p\u003e \u003cp\u003e1.5.6 Minimax Criterion 18\u003c\/p\u003e \u003cp\u003e1.5.7 Discussion 19\u003c\/p\u003e \u003cp\u003e1.6 Discriminant Functions 20\u003c\/p\u003e \u003cp\u003e1.6.1 Introduction 20\u003c\/p\u003e \u003cp\u003e1.6.2 Linear Discriminant Functions 21\u003c\/p\u003e \u003cp\u003e1.6.3 Piecewise Linear Discriminant Functions 23\u003c\/p\u003e \u003cp\u003e1.6.4 Generalised Linear Discriminant Function 24\u003c\/p\u003e \u003cp\u003e1.6.5 Summary 26\u003c\/p\u003e \u003cp\u003e1.7 Multiple Regression 27\u003c\/p\u003e \u003cp\u003e1.8 Outline of Book 29\u003c\/p\u003e \u003cp\u003e1.9 Notes and References 29\u003c\/p\u003e \u003cp\u003eExercises 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Density Estimation – Parametric 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 Estimating the Parameters of the Distributions 34\u003c\/p\u003e \u003cp\u003e2.2.1 Estimative Approach 34\u003c\/p\u003e \u003cp\u003e2.2.2 Predictive Approach 35\u003c\/p\u003e \u003cp\u003e2.3 The Gaussian Classifier 35\u003c\/p\u003e \u003cp\u003e2.3.1 Specification 35\u003c\/p\u003e \u003cp\u003e2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 37\u003c\/p\u003e \u003cp\u003e2.3.3 Example Application Study 39\u003c\/p\u003e \u003cp\u003e2.4 Dealing with Singularities in the Gaussian Classifier 40\u003c\/p\u003e \u003cp\u003e2.4.1 Introduction 40\u003c\/p\u003e \u003cp\u003e2.4.2 Na¨ive Bayes 40\u003c\/p\u003e \u003cp\u003e2.4.3 Projection onto a Subspace 41\u003c\/p\u003e \u003cp\u003e2.4.4 Linear Discriminant Function 41\u003c\/p\u003e \u003cp\u003e2.4.5 Regularised Discriminant Analysis 42\u003c\/p\u003e \u003cp\u003e2.4.6 Example Application Study 44\u003c\/p\u003e \u003cp\u003e2.4.7 Further Developments 45\u003c\/p\u003e \u003cp\u003e2.4.8 Summary 46\u003c\/p\u003e \u003cp\u003e2.5 Finite Mixture Models 46\u003c\/p\u003e \u003cp\u003e2.5.1 Introduction 46\u003c\/p\u003e \u003cp\u003e2.5.2 Mixture Models for Discrimination 48\u003c\/p\u003e \u003cp\u003e2.5.3 Parameter Estimation for Normal Mixture Models 49\u003c\/p\u003e \u003cp\u003e2.5.4 Normal Mixture Model Covariance Matrix Constraints 51\u003c\/p\u003e \u003cp\u003e2.5.5 How Many Components? 52\u003c\/p\u003e \u003cp\u003e2.5.6 Maximum Likelihood Estimation via EM 55\u003c\/p\u003e \u003cp\u003e2.5.7 Example Application Study 60\u003c\/p\u003e \u003cp\u003e2.5.8 Further Developments 62\u003c\/p\u003e \u003cp\u003e2.5.9 Summary 63\u003c\/p\u003e \u003cp\u003e2.6 Application Studies 63\u003c\/p\u003e \u003cp\u003e2.7 Summary and Discussion 66\u003c\/p\u003e \u003cp\u003e2.8 Recommendations 66\u003c\/p\u003e \u003cp\u003e2.9 Notes and References 67\u003c\/p\u003e \u003cp\u003eExercises 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Density Estimation – Bayesian 70\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 70\u003c\/p\u003e \u003cp\u003e3.1.1 Basics 72\u003c\/p\u003e \u003cp\u003e3.1.2 Recursive Calculation 72\u003c\/p\u003e \u003cp\u003e3.1.3 Proportionality 73\u003c\/p\u003e \u003cp\u003e3.2 Analytic Solutions 73\u003c\/p\u003e \u003cp\u003e3.2.1 Conjugate Priors 73\u003c\/p\u003e \u003cp\u003e3.2.2 Estimating the Mean of a Normal Distribution with Known Variance 75\u003c\/p\u003e \u003cp\u003e3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution 79\u003c\/p\u003e \u003cp\u003e3.2.4 Unknown Prior Class Probabilities 85\u003c\/p\u003e \u003cp\u003e3.2.5 Summary 87\u003c\/p\u003e \u003cp\u003e3.3 Bayesian Sampling Schemes 87\u003c\/p\u003e \u003cp\u003e3.3.1 Introduction 87\u003c\/p\u003e \u003cp\u003e3.3.2 Summarisation 87\u003c\/p\u003e \u003cp\u003e3.3.3 Sampling Version of the Bayesian Classifier 89\u003c\/p\u003e \u003cp\u003e3.3.4 Rejection Sampling 89\u003c\/p\u003e \u003cp\u003e3.3.5 Ratio of Uniforms 90\u003c\/p\u003e \u003cp\u003e3.3.6 Importance Sampling 92\u003c\/p\u003e \u003cp\u003e3.4 Markov Chain Monte Carlo Methods 95\u003c\/p\u003e \u003cp\u003e3.4.1 Introduction 95\u003c\/p\u003e \u003cp\u003e3.4.2 The Gibbs Sampler 95\u003c\/p\u003e \u003cp\u003e3.4.3 Metropolis–Hastings Algorithm 103\u003c\/p\u003e \u003cp\u003e3.4.4 Data Augmentation 107\u003c\/p\u003e \u003cp\u003e3.4.5 Reversible Jump Markov Chain Monte Carlo 108\u003c\/p\u003e \u003cp\u003e3.4.6 Slice Sampling 109\u003c\/p\u003e \u003cp\u003e3.4.7 MCMC Example – Estimation of Noisy Sinusoids 111\u003c\/p\u003e \u003cp\u003e3.4.8 Summary 115\u003c\/p\u003e \u003cp\u003e3.4.9 Notes and References 116\u003c\/p\u003e \u003cp\u003e3.5 Bayesian Approaches to Discrimination 116\u003c\/p\u003e \u003cp\u003e3.5.1 Labelled Training Data 116\u003c\/p\u003e \u003cp\u003e3.5.2 Unlabelled Training Data 117\u003c\/p\u003e \u003cp\u003e3.6 Sequential Monte Carlo Samplers 119\u003c\/p\u003e \u003cp\u003e3.6.1 Introduction 119\u003c\/p\u003e \u003cp\u003e3.6.2 Basic Methodology 121\u003c\/p\u003e \u003cp\u003e3.6.3 Summary 125\u003c\/p\u003e \u003cp\u003e3.7 Variational Bayes 126\u003c\/p\u003e \u003cp\u003e3.7.1 Introduction 126\u003c\/p\u003e \u003cp\u003e3.7.2 Description 126\u003c\/p\u003e \u003cp\u003e3.7.3 Factorised Variational Approximation 129\u003c\/p\u003e \u003cp\u003e3.7.4 Simple Example 131\u003c\/p\u003e \u003cp\u003e3.7.5 Use of the Procedure for Model Selection 135\u003c\/p\u003e \u003cp\u003e3.7.6 Further Developments and Applications 136\u003c\/p\u003e \u003cp\u003e3.7.7 Summary 137\u003c\/p\u003e \u003cp\u003e3.8 Approximate Bayesian Computation 137\u003c\/p\u003e \u003cp\u003e3.8.1 Introduction 137\u003c\/p\u003e \u003cp\u003e3.8.2 ABC Rejection Sampling 138\u003c\/p\u003e \u003cp\u003e3.8.3 ABC MCMC Sampling 140\u003c\/p\u003e \u003cp\u003e3.8.4 ABC Population Monte Carlo Sampling 141\u003c\/p\u003e \u003cp\u003e3.8.5 Model Selection 142\u003c\/p\u003e \u003cp\u003e3.8.6 Summary 143\u003c\/p\u003e \u003cp\u003e3.9 Example Application Study 144\u003c\/p\u003e \u003cp\u003e3.10 Application Studies 145\u003c\/p\u003e \u003cp\u003e3.11 Summary and Discussion 146\u003c\/p\u003e \u003cp\u003e3.12 Recommendations 147\u003c\/p\u003e \u003cp\u003e3.13 Notes and References 147\u003c\/p\u003e \u003cp\u003eExercises 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Density Estimation – Nonparametric 150\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 150\u003c\/p\u003e \u003cp\u003e4.1.1 Basic Properties of Density Estimators 150\u003c\/p\u003e \u003cp\u003e4.2 \u003ci\u003ek\u003c\/i\u003e-Nearest-Neighbour Method 152\u003c\/p\u003e \u003cp\u003e4.2.1 \u003ci\u003ek\u003c\/i\u003e-Nearest-Neighbour Classifier 152\u003c\/p\u003e \u003cp\u003e4.2.2 Derivation 154\u003c\/p\u003e \u003cp\u003e4.2.3 Choice of Distance Metric 157\u003c\/p\u003e \u003cp\u003e4.2.4 Properties of the Nearest-Neighbour Rule 159\u003c\/p\u003e \u003cp\u003e4.2.5 Linear Approximating and Eliminating Search Algorithm 159\u003c\/p\u003e \u003cp\u003e4.2.6 Branch and Bound Search Algorithms: kd-Trees 163\u003c\/p\u003e \u003cp\u003e4.2.7 Branch and Bound Search Algorithms: Ball-Trees 170\u003c\/p\u003e \u003cp\u003e4.2.8 Editing Techniques 174\u003c\/p\u003e \u003cp\u003e4.2.9 Example Application Study 177\u003c\/p\u003e \u003cp\u003e4.2.10 Further Developments 178\u003c\/p\u003e \u003cp\u003e4.2.11 Summary 179\u003c\/p\u003e \u003cp\u003e4.3 Histogram Method 180\u003c\/p\u003e \u003cp\u003e4.3.1 Data Adaptive Histograms 181\u003c\/p\u003e \u003cp\u003e4.3.2 Independence Assumption (Naïve Bayes) 181\u003c\/p\u003e \u003cp\u003e4.3.3 Lancaster Models 182\u003c\/p\u003e \u003cp\u003e4.3.4 Maximum Weight Dependence Trees 183\u003c\/p\u003e \u003cp\u003e4.3.5 Bayesian Networks 186\u003c\/p\u003e \u003cp\u003e4.3.6 Example Application Study – Naïve Bayes Text Classification 190\u003c\/p\u003e \u003cp\u003e4.3.7 Summary 193\u003c\/p\u003e \u003cp\u003e4.4 Kernel Methods 194\u003c\/p\u003e \u003cp\u003e4.4.1 Biasedness 197\u003c\/p\u003e \u003cp\u003e4.4.2 Multivariate Extension 198\u003c\/p\u003e \u003cp\u003e4.4.3 Choice of Smoothing Parameter 199\u003c\/p\u003e \u003cp\u003e4.4.4 Choice of Kernel 201\u003c\/p\u003e \u003cp\u003e4.4.5 Example Application Study 202\u003c\/p\u003e \u003cp\u003e4.4.6 Further Developments 203\u003c\/p\u003e \u003cp\u003e4.4.7 Summary 203\u003c\/p\u003e \u003cp\u003e4.5 Expansion by Basis Functions 204\u003c\/p\u003e \u003cp\u003e4.6 Copulas 207\u003c\/p\u003e \u003cp\u003e4.6.1 Introduction 207\u003c\/p\u003e \u003cp\u003e4.6.2 Mathematical Basis 207\u003c\/p\u003e \u003cp\u003e4.6.3 Copula Functions 208\u003c\/p\u003e \u003cp\u003e4.6.4 Estimating Copula Probability Density Functions 209\u003c\/p\u003e \u003cp\u003e4.6.5 Simple Example 211\u003c\/p\u003e \u003cp\u003e4.6.6 Summary 212\u003c\/p\u003e \u003cp\u003e4.7 Application Studies 213\u003c\/p\u003e \u003cp\u003e4.7.1 Comparative Studies 216\u003c\/p\u003e \u003cp\u003e4.8 Summary and Discussion 216\u003c\/p\u003e \u003cp\u003e4.9 Recommendations 217\u003c\/p\u003e \u003cp\u003e4.10 Notes and References 217\u003c\/p\u003e \u003cp\u003eExercises 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Linear Discriminant Analysis 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 221\u003c\/p\u003e \u003cp\u003e5.2 Two-Class Algorithms 222\u003c\/p\u003e \u003cp\u003e5.2.1 General Ideas 222\u003c\/p\u003e \u003cp\u003e5.2.2 Perceptron Criterion 223\u003c\/p\u003e \u003cp\u003e5.2.3 Fisher’s Criterion 227\u003c\/p\u003e \u003cp\u003e5.2.4 Least Mean-Squared-Error Procedures 228\u003c\/p\u003e \u003cp\u003e5.2.5 Further Developments 235\u003c\/p\u003e \u003cp\u003e5.2.6 Summary 235\u003c\/p\u003e \u003cp\u003e5.3 Multiclass Algorithms 236\u003c\/p\u003e \u003cp\u003e5.3.1 General Ideas 236\u003c\/p\u003e \u003cp\u003e5.3.2 Error-Correction Procedure 237\u003c\/p\u003e \u003cp\u003e5.3.3 Fisher’s Criterion – Linear Discriminant Analysis 238\u003c\/p\u003e \u003cp\u003e5.3.4 Least Mean-Squared-Error Procedures 241\u003c\/p\u003e \u003cp\u003e5.3.5 Regularisation 246\u003c\/p\u003e \u003cp\u003e5.3.6 Example Application Study 246\u003c\/p\u003e \u003cp\u003e5.3.7 Further Developments 247\u003c\/p\u003e \u003cp\u003e5.3.8 Summary 248\u003c\/p\u003e \u003cp\u003e5.4 Support Vector Machines 249\u003c\/p\u003e \u003cp\u003e5.4.1 Introduction 249\u003c\/p\u003e \u003cp\u003e5.4.2 Linearly Separable Two-Class Data 249\u003c\/p\u003e \u003cp\u003e5.4.3 Linearly Nonseparable Two-Class Data 253\u003c\/p\u003e \u003cp\u003e5.4.4 Multiclass SVMs 256\u003c\/p\u003e \u003cp\u003e5.4.5 SVMs for Regression 257\u003c\/p\u003e \u003cp\u003e5.4.6 Implementation 259\u003c\/p\u003e \u003cp\u003e5.4.7 Example Application Study 262\u003c\/p\u003e \u003cp\u003e5.4.8 Summary 263\u003c\/p\u003e \u003cp\u003e5.5 Logistic Discrimination 263\u003c\/p\u003e \u003cp\u003e5.5.1 Two-Class Case 263\u003c\/p\u003e \u003cp\u003e5.5.2 Maximum Likelihood Estimation 264\u003c\/p\u003e \u003cp\u003e5.5.3 Multiclass Logistic Discrimination 266\u003c\/p\u003e \u003cp\u003e5.5.4 Example Application Study 267\u003c\/p\u003e \u003cp\u003e5.5.5 Further Developments 267\u003c\/p\u003e \u003cp\u003e5.5.6 Summary 268\u003c\/p\u003e \u003cp\u003e5.6 Application Studies 268\u003c\/p\u003e \u003cp\u003e5.7 Summary and Discussion 268\u003c\/p\u003e \u003cp\u003e5.8 Recommendations 269\u003c\/p\u003e \u003cp\u003e5.9 Notes and References 270\u003c\/p\u003e \u003cp\u003eExercises 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Nonlinear Discriminant Analysis – Kernel and Projection Methods 274\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 274\u003c\/p\u003e \u003cp\u003e6.2 Radial Basis Functions 276\u003c\/p\u003e \u003cp\u003e6.2.1 Introduction 276\u003c\/p\u003e \u003cp\u003e6.2.2 Specifying the Model 278\u003c\/p\u003e \u003cp\u003e6.2.3 Specifying the Functional Form 278\u003c\/p\u003e \u003cp\u003e6.2.4 The Positions of the Centres 279\u003c\/p\u003e \u003cp\u003e6.2.5 Smoothing Parameters 281\u003c\/p\u003e \u003cp\u003e6.2.6 Calculation of the Weights 282\u003c\/p\u003e \u003cp\u003e6.2.7 Model Order Selection 284\u003c\/p\u003e \u003cp\u003e6.2.8 Simple RBF 285\u003c\/p\u003e \u003cp\u003e6.2.9 Motivation 286\u003c\/p\u003e \u003cp\u003e6.2.10 RBF Properties 288\u003c\/p\u003e \u003cp\u003e6.2.11 Example Application Study 288\u003c\/p\u003e \u003cp\u003e6.2.12 Further Developments 289\u003c\/p\u003e \u003cp\u003e6.2.13 Summary 290\u003c\/p\u003e \u003cp\u003e6.3 Nonlinear Support Vector Machines 291\u003c\/p\u003e \u003cp\u003e6.3.1 Introduction 291\u003c\/p\u003e \u003cp\u003e6.3.2 Binary Classification 291\u003c\/p\u003e \u003cp\u003e6.3.3 Types of Kernel 292\u003c\/p\u003e \u003cp\u003e6.3.4 Model Selection 293\u003c\/p\u003e \u003cp\u003e6.3.5 Multiclass SVMs 294\u003c\/p\u003e \u003cp\u003e6.3.6 Probability Estimates 294\u003c\/p\u003e \u003cp\u003e6.3.7 Nonlinear Regression 296\u003c\/p\u003e \u003cp\u003e6.3.8 Example Application Study 296\u003c\/p\u003e \u003cp\u003e6.3.9 Further Developments 297\u003c\/p\u003e \u003cp\u003e6.3.10 Summary 298\u003c\/p\u003e \u003cp\u003e6.4 The Multilayer Perceptron 298\u003c\/p\u003e \u003cp\u003e6.4.1 Introduction 298\u003c\/p\u003e \u003cp\u003e6.4.2 Specifying the MLP Structure 299\u003c\/p\u003e \u003cp\u003e6.4.3 Determining the MLP Weights 300\u003c\/p\u003e \u003cp\u003e6.4.4 Modelling Capacity of the MLP 307\u003c\/p\u003e \u003cp\u003e6.4.5 Logistic Classification 307\u003c\/p\u003e \u003cp\u003e6.4.6 Example Application Study 310\u003c\/p\u003e \u003cp\u003e6.4.7 Bayesian MLP Networks 311\u003c\/p\u003e \u003cp\u003e6.4.8 Projection Pursuit 313\u003c\/p\u003e \u003cp\u003e6.4.9 Summary 313\u003c\/p\u003e \u003cp\u003e6.5 Application Studies 314\u003c\/p\u003e \u003cp\u003e6.6 Summary and Discussion 316\u003c\/p\u003e \u003cp\u003e6.7 Recommendations 317\u003c\/p\u003e \u003cp\u003e6.8 Notes and References 318\u003c\/p\u003e \u003cp\u003eExercises 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Rule and Decision Tree Induction 322\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 322\u003c\/p\u003e \u003cp\u003e7.2 Decision Trees 323\u003c\/p\u003e \u003cp\u003e7.2.1 Introduction 323\u003c\/p\u003e \u003cp\u003e7.2.2 Decision Tree Construction 326\u003c\/p\u003e \u003cp\u003e7.2.3 Selection of the Splitting Rule 327\u003c\/p\u003e \u003cp\u003e7.2.4 Terminating the Splitting Procedure 330\u003c\/p\u003e \u003cp\u003e7.2.5 Assigning Class Labels to Terminal Nodes 332\u003c\/p\u003e \u003cp\u003e7.2.6 Decision Tree Pruning – Worked Example 332\u003c\/p\u003e \u003cp\u003e7.2.7 Decision Tree Construction Methods 337\u003c\/p\u003e \u003cp\u003e7.2.8 Other Issues 339\u003c\/p\u003e \u003cp\u003e7.2.9 Example Application Study 340\u003c\/p\u003e \u003cp\u003e7.2.10 Further Developments 341\u003c\/p\u003e \u003cp\u003e7.2.11 Summary 342\u003c\/p\u003e \u003cp\u003e7.3 Rule Induction 342\u003c\/p\u003e \u003cp\u003e7.3.1 Introduction 342\u003c\/p\u003e \u003cp\u003e7.3.2 Generating Rules from a Decision Tree 345\u003c\/p\u003e \u003cp\u003e7.3.3 Rule Induction Using a Sequential Covering Algorithm 345\u003c\/p\u003e \u003cp\u003e7.3.4 Example Application Study 350\u003c\/p\u003e \u003cp\u003e7.3.5 Further Developments 351\u003c\/p\u003e \u003cp\u003e7.3.6 Summary 351\u003c\/p\u003e \u003cp\u003e7.4 Multivariate Adaptive Regression Splines 351\u003c\/p\u003e \u003cp\u003e7.4.1 Introduction 351\u003c\/p\u003e \u003cp\u003e7.4.2 Recursive Partitioning Model 351\u003c\/p\u003e \u003cp\u003e7.4.3 Example Application Study 355\u003c\/p\u003e \u003cp\u003e7.4.4 Further Developments 355\u003c\/p\u003e \u003cp\u003e7.4.5 Summary 356\u003c\/p\u003e \u003cp\u003e7.5 Application Studies 356\u003c\/p\u003e \u003cp\u003e7.6 Summary and Discussion 358\u003c\/p\u003e \u003cp\u003e7.7 Recommendations 358\u003c\/p\u003e \u003cp\u003e7.8 Notes and References 359\u003c\/p\u003e \u003cp\u003eExercises 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Ensemble Methods 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 361\u003c\/p\u003e \u003cp\u003e8.2 Characterising a Classifier Combination Scheme 362\u003c\/p\u003e \u003cp\u003e8.2.1 Feature Space 363\u003c\/p\u003e \u003cp\u003e8.2.2 Level 366\u003c\/p\u003e \u003cp\u003e8.2.3 Degree of Training 368\u003c\/p\u003e \u003cp\u003e8.2.4 Form of Component Classifiers 368\u003c\/p\u003e \u003cp\u003e8.2.5 Structure 369\u003c\/p\u003e \u003cp\u003e8.2.6 Optimisation 369\u003c\/p\u003e \u003cp\u003e8.3 Data Fusion 370\u003c\/p\u003e \u003cp\u003e8.3.1 Architectures 370\u003c\/p\u003e \u003cp\u003e8.3.2 Bayesian Approaches 371\u003c\/p\u003e \u003cp\u003e8.3.3 Neyman–Pearson Formulation 373\u003c\/p\u003e \u003cp\u003e8.3.4 Trainable Rules 374\u003c\/p\u003e \u003cp\u003e8.3.5 Fixed Rules 375\u003c\/p\u003e \u003cp\u003e8.4 Classifier Combination Methods 376\u003c\/p\u003e \u003cp\u003e8.4.1 Product Rule 376\u003c\/p\u003e \u003cp\u003e8.4.2 Sum Rule 377\u003c\/p\u003e \u003cp\u003e8.4.3 Min, Max and Median Combiners 378\u003c\/p\u003e \u003cp\u003e8.4.4 Majority Vote 379\u003c\/p\u003e \u003cp\u003e8.4.5 Borda Count 379\u003c\/p\u003e \u003cp\u003e8.4.6 Combiners Trained on Class Predictions 380\u003c\/p\u003e \u003cp\u003e8.4.7 Stacked Generalisation 382\u003c\/p\u003e \u003cp\u003e8.4.8 Mixture of Experts 382\u003c\/p\u003e \u003cp\u003e8.4.9 Bagging 385\u003c\/p\u003e \u003cp\u003e8.4.10 Boosting 387\u003c\/p\u003e \u003cp\u003e8.4.11 Random Forests 389\u003c\/p\u003e \u003cp\u003e8.4.12 Model Averaging 390\u003c\/p\u003e \u003cp\u003e8.4.13 Summary of Methods 396\u003c\/p\u003e \u003cp\u003e8.4.14 Example Application Study 398\u003c\/p\u003e \u003cp\u003e8.4.15 Further Developments 399\u003c\/p\u003e \u003cp\u003e8.5 Application Studies 399\u003c\/p\u003e \u003cp\u003e8.6 Summary and Discussion 400\u003c\/p\u003e \u003cp\u003e8.7 Recommendations 401\u003c\/p\u003e \u003cp\u003e8.8 Notes and References 401\u003c\/p\u003e \u003cp\u003eExercises 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Performance Assessment 404\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 404\u003c\/p\u003e \u003cp\u003e9.2 Performance Assessment 405\u003c\/p\u003e \u003cp\u003e9.2.1 Performance Measures 405\u003c\/p\u003e \u003cp\u003e9.2.2 Discriminability 406\u003c\/p\u003e \u003cp\u003e9.2.3 Reliability 413\u003c\/p\u003e \u003cp\u003e9.2.4 ROC Curves for Performance Assessment 415\u003c\/p\u003e \u003cp\u003e9.2.5 Population and Sensor Drift 419\u003c\/p\u003e \u003cp\u003e9.2.6 Example Application Study 421\u003c\/p\u003e \u003cp\u003e9.2.7 Further Developments 422\u003c\/p\u003e \u003cp\u003e9.2.8 Summary 423\u003c\/p\u003e \u003cp\u003e9.3 Comparing Classifier Performance 424\u003c\/p\u003e \u003cp\u003e9.3.1 Which Technique is Best? 424\u003c\/p\u003e \u003cp\u003e9.3.2 Statistical Tests 425\u003c\/p\u003e \u003cp\u003e9.3.3 Comparing Rules When Misclassification Costs are Uncertain 426\u003c\/p\u003e \u003cp\u003e9.3.4 Example Application Study 428\u003c\/p\u003e \u003cp\u003e9.3.5 Further Developments 429\u003c\/p\u003e \u003cp\u003e9.3.6 Summary 429\u003c\/p\u003e \u003cp\u003e9.4 Application Studies 429\u003c\/p\u003e \u003cp\u003e9.5 Summary and Discussion 430\u003c\/p\u003e \u003cp\u003e9.6 Recommendations 430\u003c\/p\u003e \u003cp\u003e9.7 Notes and References 430\u003c\/p\u003e \u003cp\u003eExercises 431\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Feature Selection and Extraction 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 433\u003c\/p\u003e \u003cp\u003e10.2 Feature Selection 435\u003c\/p\u003e \u003cp\u003e10.2.1 Introduction 435\u003c\/p\u003e \u003cp\u003e10.2.2 Characterisation of Feature Selection Approaches 439\u003c\/p\u003e \u003cp\u003e10.2.3 Evaluation Measures 440\u003c\/p\u003e \u003cp\u003e10.2.4 Search Algorithms for Feature Subset Selection 449\u003c\/p\u003e \u003cp\u003e10.2.5 Complete Search – Branch and Bound 450\u003c\/p\u003e \u003cp\u003e10.2.6 Sequential Search 454\u003c\/p\u003e \u003cp\u003e10.2.7 Random Search 458\u003c\/p\u003e \u003cp\u003e10.2.8 Markov Blanket 459\u003c\/p\u003e \u003cp\u003e10.2.9 Stability of Feature Selection 460\u003c\/p\u003e \u003cp\u003e10.2.10 Example Application Study 462\u003c\/p\u003e \u003cp\u003e10.2.11 Further Developments 462\u003c\/p\u003e \u003cp\u003e10.2.12 Summary 463\u003c\/p\u003e \u003cp\u003e10.3 Linear Feature Extraction 463\u003c\/p\u003e \u003cp\u003e10.3.1 Principal Components Analysis 464\u003c\/p\u003e \u003cp\u003e10.3.2 Karhunen–Lo`eve Transformation 475\u003c\/p\u003e \u003cp\u003e10.3.3 Example Application Study 481\u003c\/p\u003e \u003cp\u003e10.3.4 Further Developments 482\u003c\/p\u003e \u003cp\u003e10.3.5 Summary 483\u003c\/p\u003e \u003cp\u003e10.4 Multidimensional Scaling 484\u003c\/p\u003e \u003cp\u003e10.4.1 Classical Scaling 484\u003c\/p\u003e \u003cp\u003e10.4.2 Metric MDS 486\u003c\/p\u003e \u003cp\u003e10.4.3 Ordinal Scaling 487\u003c\/p\u003e \u003cp\u003e10.4.4 Algorithms 490\u003c\/p\u003e \u003cp\u003e10.4.5 MDS for Feature Extraction 491\u003c\/p\u003e \u003cp\u003e10.4.6 Example Application Study 492\u003c\/p\u003e \u003cp\u003e10.4.7 Further Developments 493\u003c\/p\u003e \u003cp\u003e10.4.8 Summary 493\u003c\/p\u003e \u003cp\u003e10.5 Application Studies 493\u003c\/p\u003e \u003cp\u003e10.6 Summary and Discussion 495\u003c\/p\u003e \u003cp\u003e10.7 Recommendations 495\u003c\/p\u003e \u003cp\u003e10.8 Notes and References 496\u003c\/p\u003e \u003cp\u003eExercises 497\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Clustering 501\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 501\u003c\/p\u003e \u003cp\u003e11.2 Hierarchical Methods 502\u003c\/p\u003e \u003cp\u003e11.2.1 Single-Link Method 503\u003c\/p\u003e \u003cp\u003e11.2.2 Complete-Link Method 506\u003c\/p\u003e \u003cp\u003e11.2.3 Sum-of-Squares Method 507\u003c\/p\u003e \u003cp\u003e11.2.4 General Agglomerative Algorithm 508\u003c\/p\u003e \u003cp\u003e11.2.5 Properties of a Hierarchical Classification 508\u003c\/p\u003e \u003cp\u003e11.2.6 Example Application Study 509\u003c\/p\u003e \u003cp\u003e11.2.7 Summary 509\u003c\/p\u003e \u003cp\u003e11.3 Quick Partitions 510\u003c\/p\u003e \u003cp\u003e11.4 Mixture Models 511\u003c\/p\u003e \u003cp\u003e11.4.1 Model Description 511\u003c\/p\u003e \u003cp\u003e11.4.2 Example Application Study 512\u003c\/p\u003e \u003cp\u003e11.5 Sum-of-Squares Methods 513\u003c\/p\u003e \u003cp\u003e11.5.1 Clustering Criteria 514\u003c\/p\u003e \u003cp\u003e11.5.2 Clustering Algorithms 515\u003c\/p\u003e \u003cp\u003e11.5.3 Vector Quantisation 520\u003c\/p\u003e \u003cp\u003e11.5.4 Example Application Study 530\u003c\/p\u003e \u003cp\u003e11.5.5 Further Developments 530\u003c\/p\u003e \u003cp\u003e11.5.6 Summary 531\u003c\/p\u003e \u003cp\u003e11.6 Spectral Clustering 531\u003c\/p\u003e \u003cp\u003e11.6.1 Elementary Graph Theory 531\u003c\/p\u003e \u003cp\u003e11.6.2 Similarity Matrices 534\u003c\/p\u003e \u003cp\u003e11.6.3 Application to Clustering 534\u003c\/p\u003e \u003cp\u003e11.6.4 Spectral Clustering Algorithm 535\u003c\/p\u003e \u003cp\u003e11.6.5 Forms of Graph Laplacian 535\u003c\/p\u003e \u003cp\u003e11.6.6 Example Application Study 536\u003c\/p\u003e \u003cp\u003e11.6.7 Further Developments 538\u003c\/p\u003e \u003cp\u003e11.6.8 Summary 538\u003c\/p\u003e \u003cp\u003e11.7 Cluster Validity 538\u003c\/p\u003e \u003cp\u003e11.7.1 Introduction 538\u003c\/p\u003e \u003cp\u003e11.7.2 Statistical Tests 539\u003c\/p\u003e \u003cp\u003e11.7.3 Absence of Class Structure 540\u003c\/p\u003e \u003cp\u003e11.7.4 Validity of Individual Clusters 541\u003c\/p\u003e \u003cp\u003e11.7.5 Hierarchical Clustering 542\u003c\/p\u003e \u003cp\u003e11.7.6 Validation of Individual Clusterings 542\u003c\/p\u003e \u003cp\u003e11.7.7 Partitions 543\u003c\/p\u003e \u003cp\u003e11.7.8 Relative Criteria 543\u003c\/p\u003e \u003cp\u003e11.7.9 Choosing the Number of Clusters 545\u003c\/p\u003e \u003cp\u003e11.8 Application Studies 546\u003c\/p\u003e \u003cp\u003e11.9 Summary and Discussion 549\u003c\/p\u003e \u003cp\u003e11.10 Recommendations 551\u003c\/p\u003e \u003cp\u003e11.11 Notes and References 552\u003c\/p\u003e \u003cp\u003eExercises 553\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Complex Networks 555\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 555\u003c\/p\u003e \u003cp\u003e12.1.1 Characteristics 557\u003c\/p\u003e \u003cp\u003e12.1.2 Properties 557\u003c\/p\u003e \u003cp\u003e12.1.3 Questions to Address 559\u003c\/p\u003e \u003cp\u003e12.1.4 Descriptive Features 560\u003c\/p\u003e \u003cp\u003e12.1.5 Outline 560\u003c\/p\u003e \u003cp\u003e12.2 Mathematics of Networks 561\u003c\/p\u003e \u003cp\u003e12.2.1 Graph Matrices 561\u003c\/p\u003e \u003cp\u003e12.2.2 Connectivity 562\u003c\/p\u003e \u003cp\u003e12.2.3 Distance Measures 562\u003c\/p\u003e \u003cp\u003e12.2.4 Weighted Networks 563\u003c\/p\u003e \u003cp\u003e12.2.5 Centrality Measures 563\u003c\/p\u003e \u003cp\u003e12.2.6 Random Graphs 564\u003c\/p\u003e \u003cp\u003e12.3 Community Detection 565\u003c\/p\u003e \u003cp\u003e12.3.1 Clustering Methods 565\u003c\/p\u003e \u003cp\u003e12.3.2 Girvan–Newman Algorithm 568\u003c\/p\u003e \u003cp\u003e12.3.3 Modularity Approaches 570\u003c\/p\u003e \u003cp\u003e12.3.4 Local Modularity 571\u003c\/p\u003e \u003cp\u003e12.3.5 Clique Percolation 573\u003c\/p\u003e \u003cp\u003e12.3.6 Example Application Study 574\u003c\/p\u003e \u003cp\u003e12.3.7 Further Developments 575\u003c\/p\u003e \u003cp\u003e12.3.8 Summary 575\u003c\/p\u003e \u003cp\u003e12.4 Link Prediction 575\u003c\/p\u003e \u003cp\u003e12.4.1 Approaches to Link Prediction 576\u003c\/p\u003e \u003cp\u003e12.4.2 Example Application Study 578\u003c\/p\u003e \u003cp\u003e12.4.3 Further Developments 578\u003c\/p\u003e \u003cp\u003e12.5 Application Studies 579\u003c\/p\u003e \u003cp\u003e12.6 Summary and Discussion 579\u003c\/p\u003e \u003cp\u003e12.7 Recommendations 580\u003c\/p\u003e \u003cp\u003e12.8 Notes and References 580\u003c\/p\u003e \u003cp\u003eExercises 580\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Additional Topics 581\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Model Selection 581\u003c\/p\u003e \u003cp\u003e13.1.1 Separate Training and Test Sets 582\u003c\/p\u003e \u003cp\u003e13.1.2 Cross-Validation 582\u003c\/p\u003e \u003cp\u003e13.1.3 The Bayesian Viewpoint 583\u003c\/p\u003e \u003cp\u003e13.1.4 Akaike’s Information Criterion 583\u003c\/p\u003e \u003cp\u003e13.1.5 Minimum Description Length 584\u003c\/p\u003e \u003cp\u003e13.2 Missing Data 585\u003c\/p\u003e \u003cp\u003e13.3 Outlier Detection and Robust Procedures 586\u003c\/p\u003e \u003cp\u003e13.4 Mixed Continuous and Discrete Variables 587\u003c\/p\u003e \u003cp\u003e13.5 Structural Risk Minimisation and the Vapnik–Chervonenkis Dimension 588\u003c\/p\u003e \u003cp\u003e13.5.1 Bounds on the Expected Risk 588\u003c\/p\u003e \u003cp\u003e13.5.2 The VC Dimension 589\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 637\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402407682391,"sku":"9780470682272","price":107.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470682272.jpg?v=1730480307"},{"product_id":"statistical-pattern-recognition-9780470682289","title":"Statistical Pattern Recognition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.   It is a very active area of study and research, which has seen many advances in recent years.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 December 2012)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface xix\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eNotation xxiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Statistical Pattern Recognition 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Statistical Pattern Recognition 1\u003c\/p\u003e \u003cp\u003e1.1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.2 The Basic Model 2\u003c\/p\u003e \u003cp\u003e1.2 Stages in a Pattern Recognition Problem 4\u003c\/p\u003e \u003cp\u003e1.3 Issues 6\u003c\/p\u003e \u003cp\u003e1.4 Approaches to Statistical Pattern Recognition 7\u003c\/p\u003e \u003cp\u003e1.5 Elementary Decision Theory 8\u003c\/p\u003e \u003cp\u003e1.5.1 Bayes’ Decision Rule for Minimum Error 8\u003c\/p\u003e \u003cp\u003e1.5.2 Bayes’ Decision Rule for Minimum Error – Reject Option 12\u003c\/p\u003e \u003cp\u003e1.5.3 Bayes’ Decision Rule for Minimum Risk 13\u003c\/p\u003e \u003cp\u003e1.5.4 Bayes’ Decision Rule for Minimum Risk – Reject Option 15\u003c\/p\u003e \u003cp\u003e1.5.5 Neyman–Pearson Decision Rule 15\u003c\/p\u003e \u003cp\u003e1.5.6 Minimax Criterion 18\u003c\/p\u003e \u003cp\u003e1.5.7 Discussion 19\u003c\/p\u003e \u003cp\u003e1.6 Discriminant Functions 20\u003c\/p\u003e \u003cp\u003e1.6.1 Introduction 20\u003c\/p\u003e \u003cp\u003e1.6.2 Linear Discriminant Functions 21\u003c\/p\u003e \u003cp\u003e1.6.3 Piecewise Linear Discriminant Functions 23\u003c\/p\u003e \u003cp\u003e1.6.4 Generalised Linear Discriminant Function 24\u003c\/p\u003e \u003cp\u003e1.6.5 Summary 26\u003c\/p\u003e \u003cp\u003e1.7 Multiple Regression 27\u003c\/p\u003e \u003cp\u003e1.8 Outline of Book 29\u003c\/p\u003e \u003cp\u003e1.9 Notes and References 29\u003c\/p\u003e \u003cp\u003eExercises 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Density Estimation – Parametric 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 Estimating the Parameters of the Distributions 34\u003c\/p\u003e \u003cp\u003e2.2.1 Estimative Approach 34\u003c\/p\u003e \u003cp\u003e2.2.2 Predictive Approach 35\u003c\/p\u003e \u003cp\u003e2.3 The Gaussian Classifier 35\u003c\/p\u003e \u003cp\u003e2.3.1 Specification 35\u003c\/p\u003e \u003cp\u003e2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 37\u003c\/p\u003e \u003cp\u003e2.3.3 Example Application Study 39\u003c\/p\u003e \u003cp\u003e2.4 Dealing with Singularities in the Gaussian Classifier 40\u003c\/p\u003e \u003cp\u003e2.4.1 Introduction 40\u003c\/p\u003e \u003cp\u003e2.4.2 Na¨ive Bayes 40\u003c\/p\u003e \u003cp\u003e2.4.3 Projection onto a Subspace 41\u003c\/p\u003e \u003cp\u003e2.4.4 Linear Discriminant Function 41\u003c\/p\u003e \u003cp\u003e2.4.5 Regularised Discriminant Analysis 42\u003c\/p\u003e \u003cp\u003e2.4.6 Example Application Study 44\u003c\/p\u003e \u003cp\u003e2.4.7 Further Developments 45\u003c\/p\u003e \u003cp\u003e2.4.8 Summary 46\u003c\/p\u003e \u003cp\u003e2.5 Finite Mixture Models 46\u003c\/p\u003e \u003cp\u003e2.5.1 Introduction 46\u003c\/p\u003e \u003cp\u003e2.5.2 Mixture Models for Discrimination 48\u003c\/p\u003e \u003cp\u003e2.5.3 Parameter Estimation for Normal Mixture Models 49\u003c\/p\u003e \u003cp\u003e2.5.4 Normal Mixture Model Covariance Matrix Constraints 51\u003c\/p\u003e \u003cp\u003e2.5.5 How Many Components? 52\u003c\/p\u003e \u003cp\u003e2.5.6 Maximum Likelihood Estimation via EM 55\u003c\/p\u003e \u003cp\u003e2.5.7 Example Application Study 60\u003c\/p\u003e \u003cp\u003e2.5.8 Further Developments 62\u003c\/p\u003e \u003cp\u003e2.5.9 Summary 63\u003c\/p\u003e \u003cp\u003e2.6 Application Studies 63\u003c\/p\u003e \u003cp\u003e2.7 Summary and Discussion 66\u003c\/p\u003e \u003cp\u003e2.8 Recommendations 66\u003c\/p\u003e \u003cp\u003e2.9 Notes and References 67\u003c\/p\u003e \u003cp\u003eExercises 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Density Estimation – Bayesian 70\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 70\u003c\/p\u003e \u003cp\u003e3.1.1 Basics 72\u003c\/p\u003e \u003cp\u003e3.1.2 Recursive Calculation 72\u003c\/p\u003e \u003cp\u003e3.1.3 Proportionality 73\u003c\/p\u003e \u003cp\u003e3.2 Analytic Solutions 73\u003c\/p\u003e \u003cp\u003e3.2.1 Conjugate Priors 73\u003c\/p\u003e \u003cp\u003e3.2.2 Estimating the Mean of a Normal Distribution with Known Variance 75\u003c\/p\u003e \u003cp\u003e3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution 79\u003c\/p\u003e \u003cp\u003e3.2.4 Unknown Prior Class Probabilities 85\u003c\/p\u003e \u003cp\u003e3.2.5 Summary 87\u003c\/p\u003e \u003cp\u003e3.3 Bayesian Sampling Schemes 87\u003c\/p\u003e \u003cp\u003e3.3.1 Introduction 87\u003c\/p\u003e \u003cp\u003e3.3.2 Summarisation 87\u003c\/p\u003e \u003cp\u003e3.3.3 Sampling Version of the Bayesian Classifier 89\u003c\/p\u003e \u003cp\u003e3.3.4 Rejection Sampling 89\u003c\/p\u003e \u003cp\u003e3.3.5 Ratio of Uniforms 90\u003c\/p\u003e \u003cp\u003e3.3.6 Importance Sampling 92\u003c\/p\u003e \u003cp\u003e3.4 Markov Chain Monte Carlo Methods 95\u003c\/p\u003e \u003cp\u003e3.4.1 Introduction 95\u003c\/p\u003e \u003cp\u003e3.4.2 The Gibbs Sampler 95\u003c\/p\u003e \u003cp\u003e3.4.3 Metropolis–Hastings Algorithm 103\u003c\/p\u003e \u003cp\u003e3.4.4 Data Augmentation 107\u003c\/p\u003e \u003cp\u003e3.4.5 Reversible Jump Markov Chain Monte Carlo 108\u003c\/p\u003e \u003cp\u003e3.4.6 Slice Sampling 109\u003c\/p\u003e \u003cp\u003e3.4.7 MCMC Example – Estimation of Noisy Sinusoids 111\u003c\/p\u003e \u003cp\u003e3.4.8 Summary 115\u003c\/p\u003e \u003cp\u003e3.4.9 Notes and References 116\u003c\/p\u003e \u003cp\u003e3.5 Bayesian Approaches to Discrimination 116\u003c\/p\u003e \u003cp\u003e3.5.1 Labelled Training Data 116\u003c\/p\u003e \u003cp\u003e3.5.2 Unlabelled Training Data 117\u003c\/p\u003e \u003cp\u003e3.6 Sequential Monte Carlo Samplers 119\u003c\/p\u003e \u003cp\u003e3.6.1 Introduction 119\u003c\/p\u003e \u003cp\u003e3.6.2 Basic Methodology 121\u003c\/p\u003e \u003cp\u003e3.6.3 Summary 125\u003c\/p\u003e \u003cp\u003e3.7 Variational Bayes 126\u003c\/p\u003e \u003cp\u003e3.7.1 Introduction 126\u003c\/p\u003e \u003cp\u003e3.7.2 Description 126\u003c\/p\u003e \u003cp\u003e3.7.3 Factorised Variational Approximation 129\u003c\/p\u003e \u003cp\u003e3.7.4 Simple Example 131\u003c\/p\u003e \u003cp\u003e3.7.5 Use of the Procedure for Model Selection 135\u003c\/p\u003e \u003cp\u003e3.7.6 Further Developments and Applications 136\u003c\/p\u003e \u003cp\u003e3.7.7 Summary 137\u003c\/p\u003e \u003cp\u003e3.8 Approximate Bayesian Computation 137\u003c\/p\u003e \u003cp\u003e3.8.1 Introduction 137\u003c\/p\u003e \u003cp\u003e3.8.2 ABC Rejection Sampling 138\u003c\/p\u003e \u003cp\u003e3.8.3 ABC MCMC Sampling 140\u003c\/p\u003e \u003cp\u003e3.8.4 ABC Population Monte Carlo Sampling 141\u003c\/p\u003e \u003cp\u003e3.8.5 Model Selection 142\u003c\/p\u003e \u003cp\u003e3.8.6 Summary 143\u003c\/p\u003e \u003cp\u003e3.9 Example Application Study 144\u003c\/p\u003e \u003cp\u003e3.10 Application Studies 145\u003c\/p\u003e \u003cp\u003e3.11 Summary and Discussion 146\u003c\/p\u003e \u003cp\u003e3.12 Recommendations 147\u003c\/p\u003e \u003cp\u003e3.13 Notes and References 147\u003c\/p\u003e \u003cp\u003eExercises 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Density Estimation – Nonparametric 150\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 150\u003c\/p\u003e \u003cp\u003e4.1.1 Basic Properties of Density Estimators 150\u003c\/p\u003e \u003cp\u003e4.2 \u003ci\u003ek\u003c\/i\u003e-Nearest-Neighbour Method 152\u003c\/p\u003e \u003cp\u003e4.2.1 \u003ci\u003ek\u003c\/i\u003e-Nearest-Neighbour Classifier 152\u003c\/p\u003e \u003cp\u003e4.2.2 Derivation 154\u003c\/p\u003e \u003cp\u003e4.2.3 Choice of Distance Metric 157\u003c\/p\u003e \u003cp\u003e4.2.4 Properties of the Nearest-Neighbour Rule 159\u003c\/p\u003e \u003cp\u003e4.2.5 Linear Approximating and Eliminating Search Algorithm 159\u003c\/p\u003e \u003cp\u003e4.2.6 Branch and Bound Search Algorithms: kd-Trees 163\u003c\/p\u003e \u003cp\u003e4.2.7 Branch and Bound Search Algorithms: Ball-Trees 170\u003c\/p\u003e \u003cp\u003e4.2.8 Editing Techniques 174\u003c\/p\u003e \u003cp\u003e4.2.9 Example Application Study 177\u003c\/p\u003e \u003cp\u003e4.2.10 Further Developments 178\u003c\/p\u003e \u003cp\u003e4.2.11 Summary 179\u003c\/p\u003e \u003cp\u003e4.3 Histogram Method 180\u003c\/p\u003e \u003cp\u003e4.3.1 Data Adaptive Histograms 181\u003c\/p\u003e \u003cp\u003e4.3.2 Independence Assumption (Na¨ive Bayes) 181\u003c\/p\u003e \u003cp\u003e4.3.3 Lancaster Models 182\u003c\/p\u003e \u003cp\u003e4.3.4 Maximum Weight Dependence Trees 183\u003c\/p\u003e \u003cp\u003e4.3.5 Bayesian Networks 186\u003c\/p\u003e \u003cp\u003e4.3.6 Example Application Study – Na¨ive Bayes Text Classification 190\u003c\/p\u003e \u003cp\u003e4.3.7 Summary 193\u003c\/p\u003e \u003cp\u003e4.4 Kernel Methods 194\u003c\/p\u003e \u003cp\u003e4.4.1 Biasedness 197\u003c\/p\u003e \u003cp\u003e4.4.2 Multivariate Extension 198\u003c\/p\u003e \u003cp\u003e4.4.3 Choice of Smoothing Parameter 199\u003c\/p\u003e \u003cp\u003e4.4.4 Choice of Kernel 201\u003c\/p\u003e \u003cp\u003e4.4.5 Example Application Study 202\u003c\/p\u003e \u003cp\u003e4.4.6 Further Developments 203\u003c\/p\u003e \u003cp\u003e4.4.7 Summary 203\u003c\/p\u003e \u003cp\u003e4.5 Expansion by Basis Functions 204\u003c\/p\u003e \u003cp\u003e4.6 Copulas 207\u003c\/p\u003e \u003cp\u003e4.6.1 Introduction 207\u003c\/p\u003e \u003cp\u003e4.6.2 Mathematical Basis 207\u003c\/p\u003e \u003cp\u003e4.6.3 Copula Functions 208\u003c\/p\u003e \u003cp\u003e4.6.4 Estimating Copula Probability Density Functions 209\u003c\/p\u003e \u003cp\u003e4.6.5 Simple Example 211\u003c\/p\u003e \u003cp\u003e4.6.6 Summary 212\u003c\/p\u003e \u003cp\u003e4.7 Application Studies 213\u003c\/p\u003e \u003cp\u003e4.7.1 Comparative Studies 216\u003c\/p\u003e \u003cp\u003e4.8 Summary and Discussion 216\u003c\/p\u003e \u003cp\u003e4.9 Recommendations 217\u003c\/p\u003e \u003cp\u003e4.10 Notes and References 217\u003c\/p\u003e \u003cp\u003eExercises 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Linear Discriminant Analysis 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 221\u003c\/p\u003e \u003cp\u003e5.2 Two-Class Algorithms 222\u003c\/p\u003e \u003cp\u003e5.2.1 General Ideas 222\u003c\/p\u003e \u003cp\u003e5.2.2 Perceptron Criterion 223\u003c\/p\u003e \u003cp\u003e5.2.3 Fisher’s Criterion 227\u003c\/p\u003e \u003cp\u003e5.2.4 Least Mean-Squared-Error Procedures 228\u003c\/p\u003e \u003cp\u003e5.2.5 Further Developments 235\u003c\/p\u003e \u003cp\u003e5.2.6 Summary 235\u003c\/p\u003e \u003cp\u003e5.3 Multiclass Algorithms 236\u003c\/p\u003e \u003cp\u003e5.3.1 General Ideas 236\u003c\/p\u003e \u003cp\u003e5.3.2 Error-Correction Procedure 237\u003c\/p\u003e \u003cp\u003e5.3.3 Fisher’s Criterion – Linear Discriminant Analysis 238\u003c\/p\u003e \u003cp\u003e5.3.4 Least Mean-Squared-Error Procedures 241\u003c\/p\u003e \u003cp\u003e5.3.5 Regularisation 246\u003c\/p\u003e \u003cp\u003e5.3.6 Example Application Study 246\u003c\/p\u003e \u003cp\u003e5.3.7 Further Developments 247\u003c\/p\u003e \u003cp\u003e5.3.8 Summary 248\u003c\/p\u003e \u003cp\u003e5.4 Support Vector Machines 249\u003c\/p\u003e \u003cp\u003e5.4.1 Introduction 249\u003c\/p\u003e \u003cp\u003e5.4.2 Linearly Separable Two-Class Data 249\u003c\/p\u003e \u003cp\u003e5.4.3 Linearly Nonseparable Two-Class Data 253\u003c\/p\u003e \u003cp\u003e5.4.4 Multiclass SVMs 256\u003c\/p\u003e \u003cp\u003e5.4.5 SVMs for Regression 257\u003c\/p\u003e \u003cp\u003e5.4.6 Implementation 259\u003c\/p\u003e \u003cp\u003e5.4.7 Example Application Study 262\u003c\/p\u003e \u003cp\u003e5.4.8 Summary 263\u003c\/p\u003e \u003cp\u003e5.5 Logistic Discrimination 263\u003c\/p\u003e \u003cp\u003e5.5.1 Two-Class Case 263\u003c\/p\u003e \u003cp\u003e5.5.2 Maximum Likelihood Estimation 264\u003c\/p\u003e \u003cp\u003e5.5.3 Multiclass Logistic Discrimination 266\u003c\/p\u003e \u003cp\u003e5.5.4 Example Application Study 267\u003c\/p\u003e \u003cp\u003e5.5.5 Further Developments 267\u003c\/p\u003e \u003cp\u003e5.5.6 Summary 268\u003c\/p\u003e \u003cp\u003e5.6 Application Studies 268\u003c\/p\u003e \u003cp\u003e5.7 Summary and Discussion 268\u003c\/p\u003e \u003cp\u003e5.8 Recommendations 269\u003c\/p\u003e \u003cp\u003e5.9 Notes and References 270\u003c\/p\u003e \u003cp\u003eExercises 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Nonlinear Discriminant Analysis – Kernel and Projection Methods 274\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 274\u003c\/p\u003e \u003cp\u003e6.2 Radial Basis Functions 276\u003c\/p\u003e \u003cp\u003e6.2.1 Introduction 276\u003c\/p\u003e \u003cp\u003e6.2.2 Specifying the Model 278\u003c\/p\u003e \u003cp\u003e6.2.3 Specifying the Functional Form 278\u003c\/p\u003e \u003cp\u003e6.2.4 The Positions of the Centres 279\u003c\/p\u003e \u003cp\u003e6.2.5 Smoothing Parameters 281\u003c\/p\u003e \u003cp\u003e6.2.6 Calculation of the Weights 282\u003c\/p\u003e \u003cp\u003e6.2.7 Model Order Selection 284\u003c\/p\u003e \u003cp\u003e6.2.8 Simple RBF 285\u003c\/p\u003e \u003cp\u003e6.2.9 Motivation 286\u003c\/p\u003e \u003cp\u003e6.2.10 RBF Properties 288\u003c\/p\u003e \u003cp\u003e6.2.11 Example Application Study 288\u003c\/p\u003e \u003cp\u003e6.2.12 Further Developments 289\u003c\/p\u003e \u003cp\u003e6.2.13 Summary 290\u003c\/p\u003e \u003cp\u003e6.3 Nonlinear Support Vector Machines 291\u003c\/p\u003e \u003cp\u003e6.3.1 Introduction 291\u003c\/p\u003e \u003cp\u003e6.3.2 Binary Classification 291\u003c\/p\u003e \u003cp\u003e6.3.3 Types of Kernel 292\u003c\/p\u003e \u003cp\u003e6.3.4 Model Selection 293\u003c\/p\u003e \u003cp\u003e6.3.5 Multiclass SVMs 294\u003c\/p\u003e \u003cp\u003e6.3.6 Probability Estimates 294\u003c\/p\u003e \u003cp\u003e6.3.7 Nonlinear Regression 296\u003c\/p\u003e \u003cp\u003e6.3.8 Example Application Study 296\u003c\/p\u003e \u003cp\u003e6.3.9 Further Developments 297\u003c\/p\u003e \u003cp\u003e6.3.10 Summary 298\u003c\/p\u003e \u003cp\u003e6.4 The Multilayer Perceptron 298\u003c\/p\u003e \u003cp\u003e6.4.1 Introduction 298\u003c\/p\u003e \u003cp\u003e6.4.2 Specifying the MLP Structure 299\u003c\/p\u003e \u003cp\u003e6.4.3 Determining the MLP Weights 300\u003c\/p\u003e \u003cp\u003e6.4.4 Modelling Capacity of the MLP 307\u003c\/p\u003e \u003cp\u003e6.4.5 Logistic Classification 307\u003c\/p\u003e \u003cp\u003e6.4.6 Example Application Study 310\u003c\/p\u003e \u003cp\u003e6.4.7 Bayesian MLP Networks 311\u003c\/p\u003e \u003cp\u003e6.4.8 Projection Pursuit 313\u003c\/p\u003e \u003cp\u003e6.4.9 Summary 313\u003c\/p\u003e \u003cp\u003e6.5 Application Studies 314\u003c\/p\u003e \u003cp\u003e6.6 Summary and Discussion 316\u003c\/p\u003e \u003cp\u003e6.7 Recommendations 317\u003c\/p\u003e \u003cp\u003e6.8 Notes and References 318\u003c\/p\u003e \u003cp\u003eExercises 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Rule and Decision Tree Induction 322\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 322\u003c\/p\u003e \u003cp\u003e7.2 Decision Trees 323\u003c\/p\u003e \u003cp\u003e7.2.1 Introduction 323\u003c\/p\u003e \u003cp\u003e7.2.2 Decision Tree Construction 326\u003c\/p\u003e \u003cp\u003e7.2.3 Selection of the Splitting Rule 327\u003c\/p\u003e \u003cp\u003e7.2.4 Terminating the Splitting Procedure 330\u003c\/p\u003e \u003cp\u003e7.2.5 Assigning Class Labels to Terminal Nodes 332\u003c\/p\u003e \u003cp\u003e7.2.6 Decision Tree Pruning – Worked Example 332\u003c\/p\u003e \u003cp\u003e7.2.7 Decision Tree Construction Methods 337\u003c\/p\u003e \u003cp\u003e7.2.8 Other Issues 339\u003c\/p\u003e \u003cp\u003e7.2.9 Example Application Study 340\u003c\/p\u003e \u003cp\u003e7.2.10 Further Developments 341\u003c\/p\u003e \u003cp\u003e7.2.11 Summary 342\u003c\/p\u003e \u003cp\u003e7.3 Rule Induction 342\u003c\/p\u003e \u003cp\u003e7.3.1 Introduction 342\u003c\/p\u003e \u003cp\u003e7.3.2 Generating Rules from a Decision Tree 345\u003c\/p\u003e \u003cp\u003e7.3.3 Rule Induction Using a Sequential Covering Algorithm 345\u003c\/p\u003e \u003cp\u003e7.3.4 Example Application Study 350\u003c\/p\u003e \u003cp\u003e7.3.5 Further Developments 351\u003c\/p\u003e \u003cp\u003e7.3.6 Summary 351\u003c\/p\u003e \u003cp\u003e7.4 Multivariate Adaptive Regression Splines 351\u003c\/p\u003e \u003cp\u003e7.4.1 Introduction 351\u003c\/p\u003e \u003cp\u003e7.4.2 Recursive Partitioning Model 351\u003c\/p\u003e \u003cp\u003e7.4.3 Example Application Study 355\u003c\/p\u003e \u003cp\u003e7.4.4 Further Developments 355\u003c\/p\u003e \u003cp\u003e7.4.5 Summary 356\u003c\/p\u003e \u003cp\u003e7.5 Application Studies 356\u003c\/p\u003e \u003cp\u003e7.6 Summary and Discussion 358\u003c\/p\u003e \u003cp\u003e7.7 Recommendations 358\u003c\/p\u003e \u003cp\u003e7.8 Notes and References 359\u003c\/p\u003e \u003cp\u003eExercises 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Ensemble Methods 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 361\u003c\/p\u003e \u003cp\u003e8.2 Characterising a Classifier Combination Scheme 362\u003c\/p\u003e \u003cp\u003e8.2.1 Feature Space 363\u003c\/p\u003e \u003cp\u003e8.2.2 Level 366\u003c\/p\u003e \u003cp\u003e8.2.3 Degree of Training 368\u003c\/p\u003e \u003cp\u003e8.2.4 Form of Component Classifiers 368\u003c\/p\u003e \u003cp\u003e8.2.5 Structure 369\u003c\/p\u003e \u003cp\u003e8.2.6 Optimisation 369\u003c\/p\u003e \u003cp\u003e8.3 Data Fusion 370\u003c\/p\u003e \u003cp\u003e8.3.1 Architectures 370\u003c\/p\u003e \u003cp\u003e8.3.2 Bayesian Approaches 371\u003c\/p\u003e \u003cp\u003e8.3.3 Neyman–Pearson Formulation 373\u003c\/p\u003e \u003cp\u003e8.3.4 Trainable Rules 374\u003c\/p\u003e \u003cp\u003e8.3.5 Fixed Rules 375\u003c\/p\u003e \u003cp\u003e8.4 Classifier Combination Methods 376\u003c\/p\u003e \u003cp\u003e8.4.1 Product Rule 376\u003c\/p\u003e \u003cp\u003e8.4.2 Sum Rule 377\u003c\/p\u003e \u003cp\u003e8.4.3 Min, Max and Median Combiners 378\u003c\/p\u003e \u003cp\u003e8.4.4 Majority Vote 379\u003c\/p\u003e \u003cp\u003e8.4.5 Borda Count 379\u003c\/p\u003e \u003cp\u003e8.4.6 Combiners Trained on Class Predictions 380\u003c\/p\u003e \u003cp\u003e8.4.7 Stacked Generalisation 382\u003c\/p\u003e \u003cp\u003e8.4.8 Mixture of Experts 382\u003c\/p\u003e \u003cp\u003e8.4.9 Bagging 385\u003c\/p\u003e \u003cp\u003e8.4.10 Boosting 387\u003c\/p\u003e \u003cp\u003e8.4.11 Random Forests 389\u003c\/p\u003e \u003cp\u003e8.4.12 Model Averaging 390\u003c\/p\u003e \u003cp\u003e8.4.13 Summary of Methods 396\u003c\/p\u003e \u003cp\u003e8.4.14 Example Application Study 398\u003c\/p\u003e \u003cp\u003e8.4.15 Further Developments 399\u003c\/p\u003e \u003cp\u003e8.5 Application Studies 399\u003c\/p\u003e \u003cp\u003e8.6 Summary and Discussion 400\u003c\/p\u003e \u003cp\u003e8.7 Recommendations 401\u003c\/p\u003e \u003cp\u003e8.8 Notes and References 401\u003c\/p\u003e \u003cp\u003eExercises 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Performance Assessment 404\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 404\u003c\/p\u003e \u003cp\u003e9.2 Performance Assessment 405\u003c\/p\u003e \u003cp\u003e9.2.1 Performance Measures 405\u003c\/p\u003e \u003cp\u003e9.2.2 Discriminability 406\u003c\/p\u003e \u003cp\u003e9.2.3 Reliability 413\u003c\/p\u003e \u003cp\u003e9.2.4 ROC Curves for Performance Assessment 415\u003c\/p\u003e \u003cp\u003e9.2.5 Population and Sensor Drift 419\u003c\/p\u003e \u003cp\u003e9.2.6 Example Application Study 421\u003c\/p\u003e \u003cp\u003e9.2.7 Further Developments 422\u003c\/p\u003e \u003cp\u003e9.2.8 Summary 423\u003c\/p\u003e \u003cp\u003e9.3 Comparing Classifier Performance 424\u003c\/p\u003e \u003cp\u003e9.3.1 Which Technique is Best? 424\u003c\/p\u003e \u003cp\u003e9.3.2 Statistical Tests 425\u003c\/p\u003e \u003cp\u003e9.3.3 Comparing Rules When Misclassification Costs are Uncertain 426\u003c\/p\u003e \u003cp\u003e9.3.4 Example Application Study 428\u003c\/p\u003e \u003cp\u003e9.3.5 Further Developments 429\u003c\/p\u003e \u003cp\u003e9.3.6 Summary 429\u003c\/p\u003e \u003cp\u003e9.4 Application Studies 429\u003c\/p\u003e \u003cp\u003e9.5 Summary and Discussion 430\u003c\/p\u003e \u003cp\u003e9.6 Recommendations 430\u003c\/p\u003e \u003cp\u003e9.7 Notes and References 430\u003c\/p\u003e \u003cp\u003eExercises 431\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Feature Selection and Extraction 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 433\u003c\/p\u003e \u003cp\u003e10.2 Feature Selection 435\u003c\/p\u003e \u003cp\u003e10.2.1 Introduction 435\u003c\/p\u003e \u003cp\u003e10.2.2 Characterisation of Feature Selection Approaches 439\u003c\/p\u003e \u003cp\u003e10.2.3 Evaluation Measures 440\u003c\/p\u003e \u003cp\u003e10.2.4 Search Algorithms for Feature Subset Selection 449\u003c\/p\u003e \u003cp\u003e10.2.5 Complete Search – Branch and Bound 450\u003c\/p\u003e \u003cp\u003e10.2.6 Sequential Search 454\u003c\/p\u003e \u003cp\u003e10.2.7 Random Search 458\u003c\/p\u003e \u003cp\u003e10.2.8 Markov Blanket 459\u003c\/p\u003e \u003cp\u003e10.2.9 Stability of Feature Selection 460\u003c\/p\u003e \u003cp\u003e10.2.10 Example Application Study 462\u003c\/p\u003e \u003cp\u003e10.2.11 Further Developments 462\u003c\/p\u003e \u003cp\u003e10.2.12 Summary 463\u003c\/p\u003e \u003cp\u003e10.3 Linear Feature Extraction 463\u003c\/p\u003e \u003cp\u003e10.3.1 Principal Components Analysis 464\u003c\/p\u003e \u003cp\u003e10.3.2 Karhunen–Lo`eve Transformation 475\u003c\/p\u003e \u003cp\u003e10.3.3 Example Application Study 481\u003c\/p\u003e \u003cp\u003e10.3.4 Further Developments 482\u003c\/p\u003e \u003cp\u003e10.3.5 Summary 483\u003c\/p\u003e \u003cp\u003e10.4 Multidimensional Scaling 484\u003c\/p\u003e \u003cp\u003e10.4.1 Classical Scaling 484\u003c\/p\u003e \u003cp\u003e10.4.2 Metric MDS 486\u003c\/p\u003e \u003cp\u003e10.4.3 Ordinal Scaling 487\u003c\/p\u003e \u003cp\u003e10.4.4 Algorithms 490\u003c\/p\u003e \u003cp\u003e10.4.5 MDS for Feature Extraction 491\u003c\/p\u003e \u003cp\u003e10.4.6 Example Application Study 492\u003c\/p\u003e \u003cp\u003e10.4.7 Further Developments 493\u003c\/p\u003e \u003cp\u003e10.4.8 Summary 493\u003c\/p\u003e \u003cp\u003e10.5 Application Studies 493\u003c\/p\u003e \u003cp\u003e10.6 Summary and Discussion 495\u003c\/p\u003e \u003cp\u003e10.7 Recommendations 495\u003c\/p\u003e \u003cp\u003e10.8 Notes and References 496\u003c\/p\u003e \u003cp\u003eExercises 497\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Clustering 501\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 501\u003c\/p\u003e \u003cp\u003e11.2 Hierarchical Methods 502\u003c\/p\u003e \u003cp\u003e11.2.1 Single-Link Method 503\u003c\/p\u003e \u003cp\u003e11.2.2 Complete-Link Method 506\u003c\/p\u003e \u003cp\u003e11.2.3 Sum-of-Squares Method 507\u003c\/p\u003e \u003cp\u003e11.2.4 General Agglomerative Algorithm 508\u003c\/p\u003e \u003cp\u003e11.2.5 Properties of a Hierarchical Classification 508\u003c\/p\u003e \u003cp\u003e11.2.6 Example Application Study 509\u003c\/p\u003e \u003cp\u003e11.2.7 Summary 509\u003c\/p\u003e \u003cp\u003e11.3 Quick Partitions 510\u003c\/p\u003e \u003cp\u003e11.4 Mixture Models 511\u003c\/p\u003e \u003cp\u003e11.4.1 Model Description 511\u003c\/p\u003e \u003cp\u003e11.4.2 Example Application Study 512\u003c\/p\u003e \u003cp\u003e11.5 Sum-of-Squares Methods 513\u003c\/p\u003e \u003cp\u003e11.5.1 Clustering Criteria 514\u003c\/p\u003e \u003cp\u003e11.5.2 Clustering Algorithms 515\u003c\/p\u003e \u003cp\u003e11.5.3 Vector Quantisation 520\u003c\/p\u003e \u003cp\u003e11.5.4 Example Application Study 530\u003c\/p\u003e \u003cp\u003e11.5.5 Further Developments 530\u003c\/p\u003e \u003cp\u003e11.5.6 Summary 531\u003c\/p\u003e \u003cp\u003e11.6 Spectral Clustering 531\u003c\/p\u003e \u003cp\u003e11.6.1 Elementary Graph Theory 531\u003c\/p\u003e \u003cp\u003e11.6.2 Similarity Matrices 534\u003c\/p\u003e \u003cp\u003e11.6.3 Application to Clustering 534\u003c\/p\u003e \u003cp\u003e11.6.4 Spectral Clustering Algorithm 535\u003c\/p\u003e \u003cp\u003e11.6.5 Forms of Graph Laplacian 535\u003c\/p\u003e \u003cp\u003e11.6.6 Example Application Study 536\u003c\/p\u003e \u003cp\u003e11.6.7 Further Developments 538\u003c\/p\u003e \u003cp\u003e11.6.8 Summary 538\u003c\/p\u003e \u003cp\u003e11.7 Cluster Validity 538\u003c\/p\u003e \u003cp\u003e11.7.1 Introduction 538\u003c\/p\u003e \u003cp\u003e11.7.2 Statistical Tests 539\u003c\/p\u003e \u003cp\u003e11.7.3 Absence of Class Structure 540\u003c\/p\u003e \u003cp\u003e11.7.4 Validity of Individual Clusters 541\u003c\/p\u003e \u003cp\u003e11.7.5 Hierarchical Clustering 542\u003c\/p\u003e \u003cp\u003e11.7.6 Validation of Individual Clusterings 542\u003c\/p\u003e \u003cp\u003e11.7.7 Partitions 543\u003c\/p\u003e \u003cp\u003e11.7.8 Relative Criteria 543\u003c\/p\u003e \u003cp\u003e11.7.9 Choosing the Number of Clusters 545\u003c\/p\u003e \u003cp\u003e11.8 Application Studies 546\u003c\/p\u003e \u003cp\u003e11.9 Summary and Discussion 549\u003c\/p\u003e \u003cp\u003e11.10 Recommendations 551\u003c\/p\u003e \u003cp\u003e11.11 Notes and References 552\u003c\/p\u003e \u003cp\u003eExercises 553\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Complex Networks 555\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 555\u003c\/p\u003e \u003cp\u003e12.1.1 Characteristics 557\u003c\/p\u003e \u003cp\u003e12.1.2 Properties 557\u003c\/p\u003e \u003cp\u003e12.1.3 Questions to Address 559\u003c\/p\u003e \u003cp\u003e12.1.4 Descriptive Features 560\u003c\/p\u003e \u003cp\u003e12.1.5 Outline 560\u003c\/p\u003e \u003cp\u003e12.2 Mathematics of Networks 561\u003c\/p\u003e \u003cp\u003e12.2.1 Graph Matrices 561\u003c\/p\u003e \u003cp\u003e12.2.2 Connectivity 562\u003c\/p\u003e \u003cp\u003e12.2.3 Distance Measures 562\u003c\/p\u003e \u003cp\u003e12.2.4 Weighted Networks 563\u003c\/p\u003e \u003cp\u003e12.2.5 Centrality Measures 563\u003c\/p\u003e \u003cp\u003e12.2.6 Random Graphs 564\u003c\/p\u003e \u003cp\u003e12.3 Community Detection 565\u003c\/p\u003e \u003cp\u003e12.3.1 Clustering Methods 565\u003c\/p\u003e \u003cp\u003e12.3.2 Girvan–Newman Algorithm 568\u003c\/p\u003e \u003cp\u003e12.3.3 Modularity Approaches 570\u003c\/p\u003e \u003cp\u003e12.3.4 Local Modularity 571\u003c\/p\u003e \u003cp\u003e12.3.5 Clique Percolation 573\u003c\/p\u003e \u003cp\u003e12.3.6 Example Application Study 574\u003c\/p\u003e \u003cp\u003e12.3.7 Further Developments 575\u003c\/p\u003e \u003cp\u003e12.3.8 Summary 575\u003c\/p\u003e \u003cp\u003e12.4 Link Prediction 575\u003c\/p\u003e \u003cp\u003e12.4.1 Approaches to Link Prediction 576\u003c\/p\u003e \u003cp\u003e12.4.2 Example Application Study 578\u003c\/p\u003e \u003cp\u003e12.4.3 Further Developments 578\u003c\/p\u003e \u003cp\u003e12.5 Application Studies 579\u003c\/p\u003e \u003cp\u003e12.6 Summary and Discussion 579\u003c\/p\u003e \u003cp\u003e12.7 Recommendations 580\u003c\/p\u003e \u003cp\u003e12.8 Notes and References 580\u003c\/p\u003e \u003cp\u003eExercises 580\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Additional Topics 581\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Model Selection 581\u003c\/p\u003e \u003cp\u003e13.1.1 Separate Training and Test Sets 582\u003c\/p\u003e \u003cp\u003e13.1.2 Cross-Validation 582\u003c\/p\u003e \u003cp\u003e13.1.3 The Bayesian Viewpoint 583\u003c\/p\u003e \u003cp\u003e13.1.4 Akaike’s Information Criterion 583\u003c\/p\u003e \u003cp\u003e13.1.5 Minimum Description Length 584\u003c\/p\u003e \u003cp\u003e13.2 Missing Data 585\u003c\/p\u003e \u003cp\u003e13.3 Outlier Detection and Robust Procedures 586\u003c\/p\u003e \u003cp\u003e13.4 Mixed Continuous and Discrete Variables 587\u003c\/p\u003e \u003cp\u003e13.5 Structural Risk Minimisation and the Vapnik–Chervonenkis Dimension 588\u003c\/p\u003e \u003cp\u003e13.5.1 Bounds on the Expected Risk 588\u003c\/p\u003e \u003cp\u003e13.5.2 The VC Dimension 589\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 637\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402407747927,"sku":"9780470682289","price":51.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470682289.jpg?v=1730480307"},{"product_id":"pattern-classification-9780471135340","title":"Pattern Classification","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003ePATTERN CLASSIFICATION\u003cbr\u003e \u003cbr\u003e a unified view of statistical and neural approaches\u003cbr\u003e \u003cbr\u003e The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable.\u003cbr\u003e \u003cbr\u003e Offering a lucid presentation of complex app\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eStatistical Decision Theory.\u003cbr\u003e \u003cbr\u003e Need for Approximations: Fundamental Approaches.\u003cbr\u003e \u003cbr\u003e Classification Based on Statistical Models Determined by First-and-Second Order Statistical Moments.\u003cbr\u003e \u003cbr\u003e Classification Based on Mean-Square Functional Approximations.\u003cbr\u003e \u003cbr\u003e Polynomial Regression.\u003cbr\u003e \u003cbr\u003e Multilayer Perceptron Regression.\u003cbr\u003e \u003cbr\u003e Radial Basis Functions.\u003cbr\u003e \u003cbr\u003e Measurements, Features, and Feature Section.\u003cbr\u003e \u003cbr\u003e Reject Criteria and Classifier Performance.\u003cbr\u003e \u003cbr\u003e Combining Classifiers.\u003cbr\u003e \u003cbr\u003e Conclusion.\u003cbr\u003e \u003cbr\u003e STATMOD Program: Description of ftp Package.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402495500631,"sku":"9780471135340","price":150.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471135340.jpg?v=1730480585"},{"product_id":"geometric-data-analysis-an-empirical-approach-to-dimensionality-reduction-and-the-study-of-patterns-9780471239291","title":"Geometric Data Analysis An Empirical Approach to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book addresses the most efficient methods of pattern analysis using wavelet decomposition. Readers will learn to analyze data in order to emphasize the differences between closely related patterns and then categorize them in a way that is useful to system users.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"...provides a valuable summary of data reduction.\" (Technometrics, May 2002)\u003cbr\u003e \u003cbr\u003e \"...effectively describes and summarizes an emerging new field, namely, scientific data modeling and analysis.\" (Mathematical Reviews, 2003h)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.\u003cbr\u003e \u003cbr\u003e Acknowledgments.\u003cbr\u003e \u003cbr\u003e INTRODUCTION.\u003cbr\u003e \u003cbr\u003e Pattern Analysis as Data Reduction.\u003cbr\u003e \u003cbr\u003e Vector Spaces and Linear Transformations.\u003cbr\u003e \u003cbr\u003e OPTIMAL ORTHOGONAL PATTERN REPRESENTATIONS.\u003cbr\u003e \u003cbr\u003e The Karhunen-Loève Expansion.\u003cbr\u003e \u003cbr\u003e Additional Theory, Algorithms and Applications.\u003cbr\u003e \u003cbr\u003e TIME, FREQUENCY AND SCALE ANALYSIS.\u003cbr\u003e \u003cbr\u003e Fourier Analysis.\u003cbr\u003e \u003cbr\u003e Wavelet Expansions.\u003cbr\u003e \u003cbr\u003e ADAPTIVE NONLINEAR MAPPINGS.\u003cbr\u003e \u003cbr\u003e Radial Basis Functions.\u003cbr\u003e \u003cbr\u003e Neural Networks.\u003cbr\u003e \u003cbr\u003e Nonlinear Reduction Architectures.\u003cbr\u003e \u003cbr\u003e Appendix A Mathemetical Preliminaries.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402533773655,"sku":"9780471239291","price":107.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471239291.jpg?v=1730480700"},{"product_id":"speech-coding-algorithms-foundation-and-evolution-of-standardized-coders-9780471373124","title":"Speech Coding Algorithms Foundation and Evolution","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSpeech coding has evolved into a highly matured  branch of signal processing, with deployment of a  plethora of products such as cellular phones,  answering machines, communication devices, and  more recently, voice over internet protocol (VoIP).\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“…well equipped with exercises and with procedures which are helpful in implementing the coders…” (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol.1041, No.16, 2004)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcronyms xix\u003c\/p\u003e \u003cp\u003eNotation xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Overview of Speech Coding 2\u003c\/p\u003e \u003cp\u003e1.2 Classification of Speech Coders 8\u003c\/p\u003e \u003cp\u003e1.3 Speech Production and Modeling 11\u003c\/p\u003e \u003cp\u003e1.4 Some Properties of the Human Auditory System 18\u003c\/p\u003e \u003cp\u003e1.5 Speech Coding Standards 22\u003c\/p\u003e \u003cp\u003e1.6 About Algorithms 26\u003c\/p\u003e \u003cp\u003e1.7 Summary and References 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Signal Processing Techniques 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Pitch Period Estimation 33\u003c\/p\u003e \u003cp\u003e2.2 All-Pole and All-Zero Filters 45\u003c\/p\u003e \u003cp\u003e2.3 Convolution 52\u003c\/p\u003e \u003cp\u003e2.4 Summary and References 57\u003c\/p\u003e \u003cp\u003eExercises 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Stochastic Processes and Models 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Power Spectral Density 62\u003c\/p\u003e \u003cp\u003e3.2 Periodogram 67\u003c\/p\u003e \u003cp\u003e3.3 Autoregressive Model 69\u003c\/p\u003e \u003cp\u003e3.4 Autocorrelation Estimation 73\u003c\/p\u003e \u003cp\u003e3.5 Other Signal Models 85\u003c\/p\u003e \u003cp\u003e3.6 Summary and References 86\u003c\/p\u003e \u003cp\u003eExercises 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Linear Prediction 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Problem of Linear Prediction 92\u003c\/p\u003e \u003cp\u003e4.2 Linear Prediction Analysis of Nonstationary Signals 96\u003c\/p\u003e \u003cp\u003e4.3 Examples of Linear Prediction Analysis of Speech 101\u003c\/p\u003e \u003cp\u003e4.4 The Levinson–Durbin Algorithm 107\u003c\/p\u003e \u003cp\u003e4.5 The Leroux–Gueguen Algorithm 114\u003c\/p\u003e \u003cp\u003e4.6 Long-Term Linear Prediction 120\u003c\/p\u003e \u003cp\u003e4.7 Synthesis Filters 127\u003c\/p\u003e \u003cp\u003e4.8 Practical Implementation 131\u003c\/p\u003e \u003cp\u003e4.9 Moving Average Prediction 137\u003c\/p\u003e \u003cp\u003e4.10 Summary and References 138\u003c\/p\u003e \u003cp\u003eExercises 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Scalar Quantization 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 143\u003c\/p\u003e \u003cp\u003e5.2 Uniform Quantizer 147\u003c\/p\u003e \u003cp\u003e5.3 Optimal Quantizer 149\u003c\/p\u003e \u003cp\u003e5.4 Quantizer Design Algorithms 151\u003c\/p\u003e \u003cp\u003e5.5 Algorithmic Implementation 155\u003c\/p\u003e \u003cp\u003e5.6 Summary and References 158\u003c\/p\u003e \u003cp\u003eExercises 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Pulse Code Modulation and Its Variants 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Uniform Quantization 161\u003c\/p\u003e \u003cp\u003e6.2 Nonuniform Quantization 166\u003c\/p\u003e \u003cp\u003e6.3 Differential Pulse Code Modulation 172\u003c\/p\u003e \u003cp\u003e6.4 Adaptive Schemes 175\u003c\/p\u003e \u003cp\u003e6.5 Summary and References 180\u003c\/p\u003e \u003cp\u003eExercises 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Vector Quantization 184\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 185\u003c\/p\u003e \u003cp\u003e7.2 Optimal Quantizer 188\u003c\/p\u003e \u003cp\u003e7.3 Quantizer Design Algorithms 189\u003c\/p\u003e \u003cp\u003e7.4 Multistage VQ 194\u003c\/p\u003e \u003cp\u003e7.5 Predictive VQ 216\u003c\/p\u003e \u003cp\u003e7.6 Other Structured Schemes 219\u003c\/p\u003e \u003cp\u003e7.7 Summary and References 221\u003c\/p\u003e \u003cp\u003eExercises 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Scalar Quantization of Linear Prediction Coefficient 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Spectral Distortion 227\u003c\/p\u003e \u003cp\u003e8.2 Quantization Based on Reflection Coefficient and Log Area Ratio 232\u003c\/p\u003e \u003cp\u003e8.3 Line Spectral Frequency 239\u003c\/p\u003e \u003cp\u003e8.4 Quantization Based on Line Spectral Frequency 252\u003c\/p\u003e \u003cp\u003e8.5 Interpolation of LPC 256\u003c\/p\u003e \u003cp\u003e8.6 Summary and References 258\u003c\/p\u003e \u003cp\u003eExercises 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Linear Prediction Coding 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Speech Production Model 264\u003c\/p\u003e \u003cp\u003e9.2 Structure of the Algorithm 268\u003c\/p\u003e \u003cp\u003e9.3 Voicing Detector 271\u003c\/p\u003e \u003cp\u003e9.4 The FS1015 LPC Coder 275\u003c\/p\u003e \u003cp\u003e9.5 Limitations of the LPC Model 277\u003c\/p\u003e \u003cp\u003e9.6 Summary and References 280\u003c\/p\u003e \u003cp\u003eExercises 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Regular-pulse Excitation Coders 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Multipulse Excitation Model 286\u003c\/p\u003e \u003cp\u003e10.2 Regular-Pulse-Excited–Long-Term Prediction 289\u003c\/p\u003e \u003cp\u003e10.3 Summary and References 295\u003c\/p\u003e \u003cp\u003eExercises 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Code-excited Linear Prediction 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The CELP Speech Production Model 300\u003c\/p\u003e \u003cp\u003e11.2 The Principle of Analysis-by-Synthesis 301\u003c\/p\u003e \u003cp\u003e11.3 Encoding and Decoding 302\u003c\/p\u003e \u003cp\u003e11.4 Excitation Codebook Search 308\u003c\/p\u003e \u003cp\u003e11.5 Postfilter 317\u003c\/p\u003e \u003cp\u003e11.6 Summary and References 325\u003c\/p\u003e \u003cp\u003eExercises 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Federal Standard Version of CELP 330\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Improving the Long-Term Predictor 331\u003c\/p\u003e \u003cp\u003e12.2 The Concept of the Adaptive Codebook 333\u003c\/p\u003e \u003cp\u003e12.3 Incorporation of the Adaptive Codebook to the CELP Framework 336\u003c\/p\u003e \u003cp\u003e12.4 Stochastic Codebook Structure 338\u003c\/p\u003e \u003cp\u003e12.5 Adaptive Codebook Search 341\u003c\/p\u003e \u003cp\u003e12.6 Stochastic Codebook Search 344\u003c\/p\u003e \u003cp\u003e12.7 Encoder and Decoder 346\u003c\/p\u003e \u003cp\u003e12.8 Summary and References 349\u003c\/p\u003e \u003cp\u003eExercises 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Vector Sum Excited Linear Prediction 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 The Core Encoding Structure 354\u003c\/p\u003e \u003cp\u003e13.2 Search Strategies for Excitation Codebooks 356\u003c\/p\u003e \u003cp\u003e13.3 Excitation Codebook Searches 357\u003c\/p\u003e \u003cp\u003e13.4 Gain Related Procedures 362\u003c\/p\u003e \u003cp\u003e13.5 Encoder and Decoder 366\u003c\/p\u003e \u003cp\u003e13.6 Summary and References 368\u003c\/p\u003e \u003cp\u003eExercises 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Low-delay CELP 372\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Strategies to Achieve Low Delay 373\u003c\/p\u003e \u003cp\u003e14.2 Basic Operational Principles 375\u003c\/p\u003e \u003cp\u003e14.3 Linear Prediction Analysis 377\u003c\/p\u003e \u003cp\u003e14.4 Excitation Codebook Search 380\u003c\/p\u003e \u003cp\u003e14.5 Backward Gain Adaptation 385\u003c\/p\u003e \u003cp\u003e14.6 Encoder and Decoder 389\u003c\/p\u003e \u003cp\u003e14.7 Codebook Training 391\u003c\/p\u003e \u003cp\u003e14.8 Summary and References 393\u003c\/p\u003e \u003cp\u003eExercises 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Vector Quantization of Linear Prediction Coefficient 396\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Correlation Among the LSFs 396\u003c\/p\u003e \u003cp\u003e15.2 Split VQ 399\u003c\/p\u003e \u003cp\u003e15.3 Multistage VQ 403\u003c\/p\u003e \u003cp\u003e15.4 Predictive VQ 407\u003c\/p\u003e \u003cp\u003e15.5 Summary and References 418\u003c\/p\u003e \u003cp\u003eExercises 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Algebraic CELP 423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Algebraic Codebook Structure 424\u003c\/p\u003e \u003cp\u003e16.2 Adaptive Codebook 425\u003c\/p\u003e \u003cp\u003e16.3 Encoding and Decoding 433\u003c\/p\u003e \u003cp\u003e16.4 Algebraic Codebook Search 437\u003c\/p\u003e \u003cp\u003e16.5 Gain Quantization Using Conjugate VQ 443\u003c\/p\u003e \u003cp\u003e16.6 Other ACELP Standards 446\u003c\/p\u003e \u003cp\u003e16.7 Summary and References 451\u003c\/p\u003e \u003cp\u003eExercises 451\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Mixed Excitation Linear Prediction 454\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 The MELP Speech Production Model 455\u003c\/p\u003e \u003cp\u003e17.2 Fourier Magnitudes 456\u003c\/p\u003e \u003cp\u003e17.3 Shaping Filters 464\u003c\/p\u003e \u003cp\u003e17.4 Pitch Period and Voicing Strength Estimation 466\u003c\/p\u003e \u003cp\u003e17.5 Encoder Operations 474\u003c\/p\u003e \u003cp\u003e17.6 Decoder Operations 477\u003c\/p\u003e \u003cp\u003e17.7 Summary and References 481\u003c\/p\u003e \u003cp\u003eExercises 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Source-controlled Variable Bit-rate CELP 486\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Adaptive Rate Decision 487\u003c\/p\u003e \u003cp\u003e18.2 LP Analysis and LSF-Related Operations 494\u003c\/p\u003e \u003cp\u003e18.3 Decoding and Encoding 496\u003c\/p\u003e \u003cp\u003e18.4 Summary and References 498\u003c\/p\u003e \u003cp\u003eExercises 499\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Speech Quality Assessment 501\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 The Scope of Quality and Measuring Conditions 501\u003c\/p\u003e \u003cp\u003e19.2 Objective Quality Measurements for Waveform Coders 502\u003c\/p\u003e \u003cp\u003e19.3 Subjective Quality Measures 504\u003c\/p\u003e \u003cp\u003e19.4 Improvements on Objective Quality Measures 505\u003c\/p\u003e \u003cp\u003eAppendix A Minimum-phase Property of the Forward Prediction-error Filter 507\u003c\/p\u003e \u003cp\u003eAppendix B Some Properties of Line Spectral Frequency 514\u003c\/p\u003e \u003cp\u003eAppendix C Research Directions in Speech Coding 518\u003c\/p\u003e \u003cp\u003eAppendix D Linear Combiner for Pattern Classification 522\u003c\/p\u003e \u003cp\u003eAppendix E CELP: Optimal Long-term Predictor to Minimize the Weighted Difference 531\u003c\/p\u003e \u003cp\u003eAppendix F Review of Linear Algebra: Orthogonality, Basis, Linear Independence, and the Gram–schmidt Algorithm 537\u003c\/p\u003e \u003cp\u003eBibliography 542\u003c\/p\u003e \u003cp\u003eIndex 553\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402577420631,"sku":"9780471373124","price":164.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471373124.jpg?v=1730480820"},{"product_id":"the-pattern-recognition-basis-of-artificial-intelligence-9780818677960","title":"The Pattern Recognition Basis of Artificial","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"IEEE Computer Society Press,U.S.","offers":[{"title":"Default Title","offer_id":49405983949143,"sku":"9780818677960","price":95.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780818677960.jpg?v=1730494139"},{"product_id":"ai-at-the-edge-9781098120207","title":"AI at the Edge","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406792368471,"sku":"9781098120207","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098120207.jpg?v=1730497127"},{"product_id":"understanding-machine-learning-from-theory-to-algorithms-9781107057135","title":"Understanding Machine Learning From Theory to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMachine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, Germany\u003cbr\u003e'This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum, Carnegie Mellon University\u003cbr\u003e'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, Berkeley\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity trade-off; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49406808850775,"sku":"9781107057135","price":48.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107057135.jpg?v=1730497187"},{"product_id":"pattern-recognition-in-computational-molecular-biology-9781118893685","title":"Pattern Recognition in Computational Molecular","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: \u003ci\u003ePattern Recognition in Sequences\u003c\/i\u003e\u003ci\u003e, Pattern Recognition in Secondary Structures\u003c\/i\u003e\u003ci\u003e, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays\u003c\/i\u003e\u003ci\u003e, Pattern Recognition in Phylogenetic Trees, \u003c\/i\u003eand \u003ci\u003ePattern Recognition in Biological Networks\u003c\/i\u003e.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eSurveys the development of techniques and approaches on pattern recognition in biomolecular data\u003c\/li\u003e \u003cli\u003eDiscusses pattern recognit\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eLIST OF CONTRIBUTORS xxi\u003c\/p\u003e \u003cp\u003ePREFACE xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI PATTERN RECOGNITION IN SEQUENCES 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 COMBINATORIAL HAPLOTYPING PROBLEMS 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGiuseppe Lancia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction \/ 3\u003c\/p\u003e \u003cp\u003e1.2 Single Individual Haplotyping \/ 5\u003c\/p\u003e \u003cp\u003e1.2.1 The Minimum Error Correction Model \/ 8\u003c\/p\u003e \u003cp\u003e1.2.2 Probabilistic Approaches and Alternative Models \/ 10\u003c\/p\u003e \u003cp\u003e1.3 Population Haplotyping \/ 12\u003c\/p\u003e \u003cp\u003e1.3.1 Clark’s Rule \/ 14\u003c\/p\u003e \u003cp\u003e1.3.2 Pure Parsimony \/ 15\u003c\/p\u003e \u003cp\u003e1.3.3 Perfect Phylogeny \/ 19\u003c\/p\u003e \u003cp\u003e1.3.4 Disease Association \/ 21\u003c\/p\u003e \u003cp\u003e1.3.5 Other Models \/ 22\u003c\/p\u003e \u003cp\u003eReferences \/ 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 ALGORITHMIC PERSPECTIVES OF THE STRING BARCODING PROBLEMS 28\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSima Behpour and Bhaskar DasGupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction \/ 28\u003c\/p\u003e \u003cp\u003e2.2 Summary of Algorithmic Complexity Results for Barcoding Problems \/ 32\u003c\/p\u003e \u003cp\u003e2.2.1 Average Length of Optimal Barcodes \/ 33\u003c\/p\u003e \u003cp\u003e2.3 Entropy-Based Information Content Technique for Designing\u003c\/p\u003e \u003cp\u003eApproximation Algorithms for String Barcoding Problems \/ 34\u003c\/p\u003e \u003cp\u003e2.4 Techniques for Proving Inapproximability Results for String Barcoding Problems \/ 36\u003c\/p\u003e \u003cp\u003e2.4.1 Reductions from Set Covering Problem \/ 36\u003c\/p\u003e \u003cp\u003e2.4.2 Reduction from Graph-Coloring Problem \/ 38\u003c\/p\u003e \u003cp\u003e2.5 Heuristic Algorithms for String Barcoding Problems \/ 39\u003c\/p\u003e \u003cp\u003e2.5.1 Entropy-Based Method with a Different Measure for Information Content \/ 39\u003c\/p\u003e \u003cp\u003e2.5.2 Balanced Partitioning Approach \/ 40\u003c\/p\u003e \u003cp\u003e2.6 Conclusion \/ 40\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 41\u003c\/p\u003e \u003cp\u003eReferences \/ 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 ALIGNMENT-FREE MEASURES FOR WHOLE-GENOME COMPARISON 43\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMatteo Comin and Davide Verzotto\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction \/ 43\u003c\/p\u003e \u003cp\u003e3.2 Whole-Genome Sequence Analysis \/ 44\u003c\/p\u003e \u003cp\u003e3.2.1 Background on Whole-Genome Comparison \/ 44\u003c\/p\u003e \u003cp\u003e3.2.2 Alignment-Free Methods \/ 45\u003c\/p\u003e \u003cp\u003e3.2.3 Average Common Subword \/ 46\u003c\/p\u003e \u003cp\u003e3.2.4 Kullback–Leibler Information Divergence \/ 47\u003c\/p\u003e \u003cp\u003e3.3 Underlying Approach \/ 47\u003c\/p\u003e \u003cp\u003e3.3.1 Irredundant Common Subwords \/ 48\u003c\/p\u003e \u003cp\u003e3.3.2 Underlying Subwords \/ 49\u003c\/p\u003e \u003cp\u003e3.3.3 Efficient Computation of Underlying Subwords \/ 50\u003c\/p\u003e \u003cp\u003e3.3.4 Extension to Inversions and Complements \/ 53\u003c\/p\u003e \u003cp\u003e3.3.5 A Distance-Like Measure Based on Underlying Subwords \/ 53\u003c\/p\u003e \u003cp\u003e3.4 Experimental Results \/ 54\u003c\/p\u003e \u003cp\u003e3.4.1 Genome Data sets and Reference Taxonomies \/ 54\u003c\/p\u003e \u003cp\u003e3.4.2 Whole-Genome Phylogeny Reconstruction \/ 56\u003c\/p\u003e \u003cp\u003e3.5 Conclusion \/ 61\u003c\/p\u003e \u003cp\u003eAuthor’s Contributions \/ 62\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 62\u003c\/p\u003e \u003cp\u003eReferences \/ 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A MAXIMUM LIKELIHOOD FRAMEWORK FOR MULTIPLE SEQUENCE LOCAL ALIGNMENT 65\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChengpeng Bi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction \/ 65\u003c\/p\u003e \u003cp\u003e4.2 Multiple Sequence Local Alignment \/ 67\u003c\/p\u003e \u003cp\u003e4.2.1 Overall Objective Function \/ 67\u003c\/p\u003e \u003cp\u003e4.2.2 Maximum Likelihood Model \/ 68\u003c\/p\u003e \u003cp\u003e4.3 Motif Finding Algorithms \/ 70\u003c\/p\u003e \u003cp\u003e4.3.1 DEM Motif Algorithm \/ 70\u003c\/p\u003e \u003cp\u003e4.3.2 WEM Motif Finding Algorithm \/ 70\u003c\/p\u003e \u003cp\u003e4.3.3 Metropolis Motif Finding Algorithm \/ 72\u003c\/p\u003e \u003cp\u003e4.3.4 Gibbs Motif Finding Algorithm \/ 73\u003c\/p\u003e \u003cp\u003e4.3.5 Pseudo-Gibbs Motif Finding Algorithm \/ 74\u003c\/p\u003e \u003cp\u003e4.4 Time Complexity \/ 75\u003c\/p\u003e \u003cp\u003e4.5 Case Studies \/ 75\u003c\/p\u003e \u003cp\u003e4.5.1 Performance Evaluation \/ 76\u003c\/p\u003e \u003cp\u003e4.5.2 CRP Binding Sites \/ 76\u003c\/p\u003e \u003cp\u003e4.5.3 Multiple Motifs in Helix–Turn–Helix Protein Structure \/ 78\u003c\/p\u003e \u003cp\u003e4.6 Conclusion \/ 80\u003c\/p\u003e \u003cp\u003eReferences \/ 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 GLOBAL SEQUENCE ALIGNMENT WITH A BOUNDED NUMBER OF GAPS 83\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eCarl Barton, Tomáš Flouri, Costas S. Iliopoulos, and Solon P. Pissis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction \/ 83\u003c\/p\u003e \u003cp\u003e5.2 Definitions and Notation \/ 85\u003c\/p\u003e \u003cp\u003e5.3 Problem Definition \/ 87\u003c\/p\u003e \u003cp\u003e5.4 Algorithms \/ 88\u003c\/p\u003e \u003cp\u003e5.5 Conclusion \/ 94\u003c\/p\u003e \u003cp\u003eReferences \/ 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII PATTERN RECOGNITION IN SECONDARY STRUCTURES 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A SHORT REVIEW ON PROTEIN SECONDARY STRUCTURE PREDICTION METHODS 99\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRenxiang Yan, Jiangning Song, Weiwen Cai, and Ziding Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction \/ 99\u003c\/p\u003e \u003cp\u003e6.2 Representative Protein Secondary Structure Prediction Methods \/ 102\u003c\/p\u003e \u003cp\u003e6.2.1 Chou–Fasman \/ 103\u003c\/p\u003e \u003cp\u003e6.2.2 GOR \/ 104\u003c\/p\u003e \u003cp\u003e6.2.3 PHD \/ 104\u003c\/p\u003e \u003cp\u003e6.2.4 PSIPRED \/ 104\u003c\/p\u003e \u003cp\u003e6.2.5 SPINE-X \/ 105\u003c\/p\u003e \u003cp\u003e6.2.6 PSSpred \/ 105\u003c\/p\u003e \u003cp\u003e6.2.7 Meta Methods \/ 105\u003c\/p\u003e \u003cp\u003e6.3 Evaluation of Protein Secondary Structure Prediction Methods \/ 106\u003c\/p\u003e \u003cp\u003e6.3.1 Measures \/ 106\u003c\/p\u003e \u003cp\u003e6.3.2 Benchmark \/ 106\u003c\/p\u003e \u003cp\u003e6.3.3 Performances \/ 107\u003c\/p\u003e \u003cp\u003e6.4 Conclusion \/ 110\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 110\u003c\/p\u003e \u003cp\u003eReferences \/ 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A GENERIC APPROACH TO BIOLOGICAL SEQUENCE SEGMENTATION PROBLEMS: APPLICATION TO PROTEIN SECONDARY STRUCTURE PREDICTION 114\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYann Guermeur and Fabien Lauer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction \/ 114\u003c\/p\u003e \u003cp\u003e7.2 Biological Sequence Segmentation \/ 115\u003c\/p\u003e \u003cp\u003e7.3 MSVMpred \/ 117\u003c\/p\u003e \u003cp\u003e7.3.1 Base Classifiers \/ 117\u003c\/p\u003e \u003cp\u003e7.3.2 Ensemble Methods \/ 118\u003c\/p\u003e \u003cp\u003e7.3.3 Convex Combination \/ 119\u003c\/p\u003e \u003cp\u003e7.4 Postprocessing with A Generative Model \/ 119\u003c\/p\u003e \u003cp\u003e7.5 Dedication to Protein Secondary Structure Prediction \/ 120\u003c\/p\u003e \u003cp\u003e7.5.1 Biological Problem \/ 121\u003c\/p\u003e \u003cp\u003e7.5.2 MSVMpred2 \/ 121\u003c\/p\u003e \u003cp\u003e7.5.3 Hidden Semi-Markov Model \/ 122\u003c\/p\u003e \u003cp\u003e7.5.4 Experimental Results \/ 122\u003c\/p\u003e \u003cp\u003e7.6 Conclusions and Ongoing Research \/ 125\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 126\u003c\/p\u003e \u003cp\u003eReferences \/ 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 STRUCTURAL MOTIF IDENTIFICATION AND RETRIEVAL: A GEOMETRICAL APPROACH 129\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVirginio Cantoni, Marco Ferretti, Mirto Musci, and Nahumi Nugrahaningsih\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction \/ 129\u003c\/p\u003e \u003cp\u003e8.2 A Few Basic Concepts \/ 130\u003c\/p\u003e \u003cp\u003e8.2.1 Hierarchy of Protein Structures \/ 130\u003c\/p\u003e \u003cp\u003e8.2.2 Secondary Structure Elements \/ 131\u003c\/p\u003e \u003cp\u003e8.2.3 Structural Motifs \/ 132\u003c\/p\u003e \u003cp\u003e8.2.4 Available Sources for Protein Data \/ 134\u003c\/p\u003e \u003cp\u003e8.3 State of the Art \/ 135\u003c\/p\u003e \u003cp\u003e8.3.1 Protein Structure Motif Search \/ 135\u003c\/p\u003e \u003cp\u003e8.3.2 Promotif \/ 136\u003c\/p\u003e \u003cp\u003e8.3.3 Secondary-Structure Matching \/ 137\u003c\/p\u003e \u003cp\u003e8.3.4 Multiple Structural Alignment by Secondary Structures \/ 138\u003c\/p\u003e \u003cp\u003e8.4 A Novel Geometrical Approach to Motif Retrieval \/ 138\u003c\/p\u003e \u003cp\u003e8.4.1 Secondary Structures Cooccurrences \/ 138\u003c\/p\u003e \u003cp\u003e8.4.2 Cross Motif Search \/ 143\u003c\/p\u003e \u003cp\u003e8.4.3 Complete Cross Motif Search \/ 146\u003c\/p\u003e \u003cp\u003e8.5 Implementation Notes \/ 149\u003c\/p\u003e \u003cp\u003e8.5.1 Optimizations \/ 149\u003c\/p\u003e \u003cp\u003e8.5.2 Parallel Approaches \/ 150\u003c\/p\u003e \u003cp\u003e8.6 Conclusions and Future Work \/ 151\u003c\/p\u003e \u003cp\u003eAcknowledgment \/ 152\u003c\/p\u003e \u003cp\u003eReferences \/ 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 GENOME-WIDE SEARCH FOR PSEUDOKNOTTED NONCODING RNAs: A COMPARATIVE STUDY 155\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMeghana Vasavada, Kevin Byron, Yang Song, and Jason T.L. Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction \/ 155\u003c\/p\u003e \u003cp\u003e9.2 Background \/ 156\u003c\/p\u003e \u003cp\u003e9.2.1 Noncoding RNAs and Their Secondary Structures \/ 156\u003c\/p\u003e \u003cp\u003e9.2.2 Pseudoknotted ncRNA Search Tools \/ 157\u003c\/p\u003e \u003cp\u003e9.3 Methodology \/ 157\u003c\/p\u003e \u003cp\u003e9.4 Results and Interpretation \/ 161\u003c\/p\u003e \u003cp\u003e9.5 Conclusion \/ 162\u003c\/p\u003e \u003cp\u003eReferences \/ 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII PATTERN RECOGNITION IN TERTIARY STRUCTURES 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 MOTIF DISCOVERY IN PROTEIN 3D-STRUCTURES USING GRAPH MINING TECHNIQUES 167\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eWajdi Dhifli and Engelbert Mephu Nguifo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction \/ 167\u003c\/p\u003e \u003cp\u003e10.2 From Protein 3D-Structures to Protein Graphs \/ 169\u003c\/p\u003e \u003cp\u003e10.2.1 Parsing Protein 3D-Structures into Graphs \/ 169\u003c\/p\u003e \u003cp\u003e10.3 Graph Mining \/ 172\u003c\/p\u003e \u003cp\u003e10.4 Subgraph Mining \/ 173\u003c\/p\u003e \u003cp\u003e10.5 Frequent Subgraph Discovery \/ 173\u003c\/p\u003e \u003cp\u003e10.5.1 Problem Definition \/ 174\u003c\/p\u003e \u003cp\u003e10.5.2 Candidates Generation \/ 176\u003c\/p\u003e \u003cp\u003e10.5.3 Frequent Subgraph Discovery Approaches \/ 177\u003c\/p\u003e \u003cp\u003e10.5.4 Variants of Frequent Subgraph Mining: Closed and Maximal Subgraphs \/ 178\u003c\/p\u003e \u003cp\u003e10.6 Feature Selection \/ 179\u003c\/p\u003e \u003cp\u003e10.6.1 Relevance of a Feature \/ 179\u003c\/p\u003e \u003cp\u003e10.7 Feature Selection for Subgraphs \/ 180\u003c\/p\u003e \u003cp\u003e10.7.1 Problem Statement \/ 180\u003c\/p\u003e \u003cp\u003e10.7.2 Mining Top-k Subgraphs \/ 180\u003c\/p\u003e \u003cp\u003e10.7.3 Clustering-Based Subgraph Selection \/ 181\u003c\/p\u003e \u003cp\u003e10.7.4 Sampling-Based Approaches \/ 181\u003c\/p\u003e \u003cp\u003e10.7.5 Approximate Subgraph Mining \/ 181\u003c\/p\u003e \u003cp\u003e10.7.6 Discriminative Subgraph Selection \/ 182\u003c\/p\u003e \u003cp\u003e10.7.7 Other Significant Subgraph Selection Approaches \/ 182\u003c\/p\u003e \u003cp\u003e10.8 Discussion \/ 183\u003c\/p\u003e \u003cp\u003e10.9 Conclusion \/ 185\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 185\u003c\/p\u003e \u003cp\u003eReferences \/ 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 FUZZY AND UNCERTAIN LEARNING TECHNIQUES FOR THE ANALYSIS AND PREDICTION OF PROTEIN TERTIARY STRUCTURES 190\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChinua Umoja, Xiaxia Yu, and Robert Harrison\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction \/ 190\u003c\/p\u003e \u003cp\u003e11.2 Genetic Algorithms \/ 192\u003c\/p\u003e \u003cp\u003e11.2.1 GA Model Selection in Protein Structure Prediction \/ 196\u003c\/p\u003e \u003cp\u003e11.2.2 Common Methodology \/ 198\u003c\/p\u003e \u003cp\u003e11.3 Supervised Machine Learning Algorithm \/ 201\u003c\/p\u003e \u003cp\u003e11.3.1 Artificial Neural Networks \/ 201\u003c\/p\u003e \u003cp\u003e11.3.2 ANNs in Protein Structure Prediction \/ 202\u003c\/p\u003e \u003cp\u003e11.3.3 Support Vector Machines \/ 203\u003c\/p\u003e \u003cp\u003e11.4 Fuzzy Application \/ 204\u003c\/p\u003e \u003cp\u003e11.4.1 Fuzzy Logic \/ 204\u003c\/p\u003e \u003cp\u003e11.4.2 Fuzzy SVMs \/ 204\u003c\/p\u003e \u003cp\u003e11.4.3 Adaptive-Network-Based Fuzzy Inference Systems \/ 205\u003c\/p\u003e \u003cp\u003e11.4.4 Fuzzy Decision Trees \/ 206\u003c\/p\u003e \u003cp\u003e11.5 Conclusion \/ 207\u003c\/p\u003e \u003cp\u003eReferences \/ 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 PROTEIN INTER-DOMAIN LINKER PREDICTION 212\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMaad Shatnawi, Paul D. Yoo, and Sami Muhaidat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction \/ 212\u003c\/p\u003e \u003cp\u003e12.2 Protein Structure Overview \/ 213\u003c\/p\u003e \u003cp\u003e12.3 Technical Challenges and Open Issues \/ 214\u003c\/p\u003e \u003cp\u003e12.4 Prediction Assessment \/ 215\u003c\/p\u003e \u003cp\u003e12.5 Current Approaches \/ 216\u003c\/p\u003e \u003cp\u003e12.5.1 DomCut \/ 216\u003c\/p\u003e \u003cp\u003e12.5.2 Scooby-Domain \/ 217\u003c\/p\u003e \u003cp\u003e12.5.3 FIEFDom \/ 218\u003c\/p\u003e \u003cp\u003e12.5.4 Chatterjee et al. (2009) \/ 219\u003c\/p\u003e \u003cp\u003e12.5.5 Drop \/ 219\u003c\/p\u003e \u003cp\u003e12.6 Domain Boundary Prediction Using Enhanced General Regression Network \/ 220\u003c\/p\u003e \u003cp\u003e12.6.1 Multi-Domain Benchmark Data Set \/ 220\u003c\/p\u003e \u003cp\u003e12.6.2 Compact Domain Profile \/ 221\u003c\/p\u003e \u003cp\u003e12.6.3 The Enhanced Semi-Parametric Model \/ 222\u003c\/p\u003e \u003cp\u003e12.6.4 Training, Testing, and Validation \/ 225\u003c\/p\u003e \u003cp\u003e12.6.5 Experimental Results \/ 226\u003c\/p\u003e \u003cp\u003e12.7 Inter-Domain Linkers Prediction Using Compositional Index and Simulated Annealing \/ 227\u003c\/p\u003e \u003cp\u003e12.7.1 Compositional Index \/ 228\u003c\/p\u003e \u003cp\u003e12.7.2 Detecting the Optimal Set of Threshold Values Using Simulated Annealing \/ 229\u003c\/p\u003e \u003cp\u003e12.7.3 Experimental Results \/ 230\u003c\/p\u003e \u003cp\u003e12.8 Conclusion \/ 232\u003c\/p\u003e \u003cp\u003eReferences \/ 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 PREDICTION OF PROLINE CIS–TRANS ISOMERIZATION 236\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePaul D. Yoo, Maad Shatnawi, Sami Muhaidat, Kamal Taha, and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction \/ 236\u003c\/p\u003e \u003cp\u003e13.2 Methods \/ 238\u003c\/p\u003e \u003cp\u003e13.2.1 Evolutionary Data Set Construction \/ 238\u003c\/p\u003e \u003cp\u003e13.2.2 Protein Secondary Structure Information \/ 239\u003c\/p\u003e \u003cp\u003e13.2.3 Method I: Intelligent Voting \/ 239\u003c\/p\u003e \u003cp\u003e13.2.4 Method II: Randomized Meta-Learning \/ 241\u003c\/p\u003e \u003cp\u003e13.2.5 Model Validation and Testing \/ 242\u003c\/p\u003e \u003cp\u003e13.2.6 Parameter Tuning \/ 242\u003c\/p\u003e \u003cp\u003e13.3 Model Evaluation and Analysis \/ 243\u003c\/p\u003e \u003cp\u003e13.4 Conclusion \/ 245\u003c\/p\u003e \u003cp\u003eReferences \/ 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV PATTERN RECOGNITION IN QUATERNARY STRUCTURES 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 PREDICTION OF PROTEIN QUATERNARY STRUCTURES 251\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAkbar Vaseghi, Maryam Faridounnia, Soheila Shokrollahzade, Samad Jahandideh, and Kuo-Chen Chou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction \/ 251\u003c\/p\u003e \u003cp\u003e14.2 Protein Structure Prediction \/ 255\u003c\/p\u003e \u003cp\u003e14.2.1 Secondary Structure Prediction \/ 255\u003c\/p\u003e \u003cp\u003e14.2.2 Modeling of Tertiary Structure \/ 256\u003c\/p\u003e \u003cp\u003e14.3 Template-Based Predictions \/ 257\u003c\/p\u003e \u003cp\u003e14.3.1 Homology Modeling \/ 257\u003c\/p\u003e \u003cp\u003e14.3.2 Threading Methods \/ 257\u003c\/p\u003e \u003cp\u003e14.3.3 Ab initio Modeling \/ 257\u003c\/p\u003e \u003cp\u003e14.4 Critical Assessment of Protein Structure Prediction \/ 258\u003c\/p\u003e \u003cp\u003e14.5 Quaternary Structure Prediction \/ 258\u003c\/p\u003e \u003cp\u003e14.6 Conclusion \/ 261\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 261\u003c\/p\u003e \u003cp\u003eReferences \/ 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 COMPARISON OF PROTEIN QUATERNARY STRUCTURES BY GRAPH APPROACHES 266\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSheng-Lung Peng and Yu-Wei Tsay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction \/ 266\u003c\/p\u003e \u003cp\u003e15.2 Similarity in the Graph Model \/ 268\u003c\/p\u003e \u003cp\u003e15.2.1 Graph Model for Proteins \/ 270\u003c\/p\u003e \u003cp\u003e15.3 Measuring Structural Similarity VIA MCES \/ 272\u003c\/p\u003e \u003cp\u003e15.3.1 Problem Formulation \/ 273\u003c\/p\u003e \u003cp\u003e15.3.2 Constructing P-Graphs \/ 274\u003c\/p\u003e \u003cp\u003e15.3.3 Constructing Line Graphs \/ 276\u003c\/p\u003e \u003cp\u003e15.3.4 Constructing Modular Graphs \/ 276\u003c\/p\u003e \u003cp\u003e15.3.5 Maximum Clique Detection \/ 277\u003c\/p\u003e \u003cp\u003e15.3.6 Experimental Results \/ 277\u003c\/p\u003e \u003cp\u003e15.4 Protein Comparison VIA Graph Spectra \/ 279\u003c\/p\u003e \u003cp\u003e15.4.1 Graph Spectra \/ 279\u003c\/p\u003e \u003cp\u003e15.4.2 Matrix Selection \/ 281\u003c\/p\u003e \u003cp\u003e15.4.3 Graph Cospectrality and Similarity \/ 283\u003c\/p\u003e \u003cp\u003e15.4.4 Cospectral Comparison \/ 283\u003c\/p\u003e \u003cp\u003e15.4.5 Experimental Results \/ 284\u003c\/p\u003e \u003cp\u003e15.5 Conclusion \/ 287\u003c\/p\u003e \u003cp\u003eReferences \/ 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 STRUCTURAL DOMAINS IN PREDICTION OF BIOLOGICAL PROTEIN–PROTEIN INTERACTIONS 291\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMina Maleki, Michael Hall, and Luis Rueda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction \/ 291\u003c\/p\u003e \u003cp\u003e16.2 Structural Domains \/ 293\u003c\/p\u003e \u003cp\u003e16.3 The Prediction Framework \/ 293\u003c\/p\u003e \u003cp\u003e16.4 Feature Extraction and Prediction Properties \/ 294\u003c\/p\u003e \u003cp\u003e16.4.1 Physicochemical Properties \/ 296\u003c\/p\u003e \u003cp\u003e16.4.2 Domain-Based Properties \/ 298\u003c\/p\u003e \u003cp\u003e16.5 Feature Selection \/ 299\u003c\/p\u003e \u003cp\u003e16.5.1 Filter Methods \/ 299\u003c\/p\u003e \u003cp\u003e16.5.2 Wrapper Methods \/ 301\u003c\/p\u003e \u003cp\u003e16.6 Classification \/ 301\u003c\/p\u003e \u003cp\u003e16.6.1 Linear Dimensionality Reduction \/ 301\u003c\/p\u003e \u003cp\u003e16.6.2 Support Vector Machines \/ 303\u003c\/p\u003e \u003cp\u003e16.6.3 k-Nearest Neighbor \/ 303\u003c\/p\u003e \u003cp\u003e16.6.4 Naive Bayes \/ 304\u003c\/p\u003e \u003cp\u003e16.7 Evaluation and Analysis \/ 304\u003c\/p\u003e \u003cp\u003e16.8 Results and Discussion \/ 304\u003c\/p\u003e \u003cp\u003e16.8.1 Analysis of the Prediction Properties \/ 304\u003c\/p\u003e \u003cp\u003e16.8.2 Analysis of Structural DDIs \/ 307\u003c\/p\u003e \u003cp\u003e16.9 Conclusion \/ 309\u003c\/p\u003e \u003cp\u003eReferences \/ 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV PATTERN RECOGNITION IN MICROARRAYS 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 CONTENT-BASED RETRIEVAL OF MICROARRAY EXPERIMENTS 317\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHasan O¢gul\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction \/ 317\u003c\/p\u003e \u003cp\u003e17.2 Information Retrieval: Terminology and Background \/ 318\u003c\/p\u003e \u003cp\u003e17.3 Content-Based Retrieval \/ 320\u003c\/p\u003e \u003cp\u003e17.4 Microarray Data and Databases \/ 322\u003c\/p\u003e \u003cp\u003e17.5 Methods for Retrieving Microarray Experiments \/ 324\u003c\/p\u003e \u003cp\u003e17.6 Similarity Metrics \/ 327\u003c\/p\u003e \u003cp\u003e17.7 Evaluating Retrieval Performance \/ 329\u003c\/p\u003e \u003cp\u003e17.8 Software Tools \/ 330\u003c\/p\u003e \u003cp\u003e17.9 Conclusion and Future Directions \/ 331\u003c\/p\u003e \u003cp\u003eAcknowledgment \/ 332\u003c\/p\u003e \u003cp\u003eReferences \/ 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 EXTRACTION OF DIFFERENTIALLY EXPRESSED GENES IN MICROARRAY DATA 335\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eTiratha Raj Singh, Brigitte Vannier, and Ahmed Moussa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction \/ 335\u003c\/p\u003e \u003cp\u003e18.2 From Microarray Image to Signal \/ 336\u003c\/p\u003e \u003cp\u003e18.2.1 Signal from Oligo DNA Array Image \/ 336\u003c\/p\u003e \u003cp\u003e18.2.2 Signal from Two-Color cDNA Array \/ 337\u003c\/p\u003e \u003cp\u003e18.3 Microarray Signal Analysis \/ 337\u003c\/p\u003e \u003cp\u003e18.3.1 Absolute Analysis and Replicates in Microarrays \/ 338\u003c\/p\u003e \u003cp\u003e18.3.2 Microarray Normalization \/ 339\u003c\/p\u003e \u003cp\u003e18.4 Algorithms for De Gene Selection \/ 339\u003c\/p\u003e \u003cp\u003e18.4.1 Within–Between DE Gene (WB-DEG) Selection Algorithm \/ 340\u003c\/p\u003e \u003cp\u003e18.4.2 Comparison of the WB-DEGs with Two Classical DE Gene Selection Methods on Latin Square Data \/ 341\u003c\/p\u003e \u003cp\u003e18.5 Gene Ontology Enrichment and Gene Set Enrichment Analysis \/ 343\u003c\/p\u003e \u003cp\u003e18.6 Conclusion \/ 345\u003c\/p\u003e \u003cp\u003eReferences \/ 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 CLUSTERING AND CLASSIFICATION TECHNIQUES FOR GENE EXPRESSION PROFILE PATTERN ANALYSIS 347\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEmanuel Weitschek, Giulia Fiscon, Valentina Fustaino, Giovanni Felici, and Paola Bertolazzi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction \/ 347\u003c\/p\u003e \u003cp\u003e19.2 Transcriptome Analysis \/ 348\u003c\/p\u003e \u003cp\u003e19.3 Microarrays \/ 349\u003c\/p\u003e \u003cp\u003e19.3.1 Applications \/ 349\u003c\/p\u003e \u003cp\u003e19.3.2 Microarray Technology \/ 350\u003c\/p\u003e \u003cp\u003e19.3.3 Microarray Workflow \/ 350\u003c\/p\u003e \u003cp\u003e19.4 RNA-Seq \/ 351\u003c\/p\u003e \u003cp\u003e19.5 Benefits and Drawbacks of RNA-Seq and Microarray Technologies \/ 353\u003c\/p\u003e \u003cp\u003e19.6 Gene Expression Profile Analysis \/ 356\u003c\/p\u003e \u003cp\u003e19.6.1 Data Definition \/ 356\u003c\/p\u003e \u003cp\u003e19.6.2 Data Analysis \/ 357\u003c\/p\u003e \u003cp\u003e19.6.3 Normalization and Background Correction \/ 357\u003c\/p\u003e \u003cp\u003e19.6.4 Genes Clustering \/ 359\u003c\/p\u003e \u003cp\u003e19.6.5 Experiment Classification \/ 361\u003c\/p\u003e \u003cp\u003e19.6.6 Software Tools for Gene Expression Profile Analysis \/ 362\u003c\/p\u003e \u003cp\u003e19.7 Real Case Studies \/ 364\u003c\/p\u003e \u003cp\u003e19.8 Conclusions \/ 367\u003c\/p\u003e \u003cp\u003eReferences \/ 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 MINING INFORMATIVE PATTERNS IN MICROARRAY DATA 371\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLi Teng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction \/ 371\u003c\/p\u003e \u003cp\u003e20.2 Patterns with Similarity \/ 373\u003c\/p\u003e \u003cp\u003e20.2.1 Similarity Measurement \/ 374\u003c\/p\u003e \u003cp\u003e20.2.2 Clustering \/ 376\u003c\/p\u003e \u003cp\u003e20.2.3 Biclustering \/ 379\u003c\/p\u003e \u003cp\u003e20.2.4 Types of Biclusters \/ 380\u003c\/p\u003e \u003cp\u003e20.2.5 Measurement of the Homogeneity \/ 383\u003c\/p\u003e \u003cp\u003e20.2.6 Biclustering Algorithms with Different Searching Schemes \/ 387\u003c\/p\u003e \u003cp\u003e20.3 Conclusion \/ 391\u003c\/p\u003e \u003cp\u003eReferences \/ 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 ARROW PLOT AND CORRESPONDENCE ANALYSIS MAPS FOR VISUALIZING THE EFFECTS OF BACKGROUND CORRECTION AND NORMALIZATION METHODS ON MICROARRAY DATA 394\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eCarina Silva, Adelaide Freitas, Sara Roque, and Lisete Sousa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Overview \/ 394\u003c\/p\u003e \u003cp\u003e21.1.1 Background Correction Methods \/ 395\u003c\/p\u003e \u003cp\u003e21.1.2 Normalization Methods \/ 396\u003c\/p\u003e \u003cp\u003e21.1.3 Literature Review \/ 397\u003c\/p\u003e \u003cp\u003e21.2 Arrow Plot \/ 399\u003c\/p\u003e \u003cp\u003e21.2.1 DE Genes Versus Special Genes \/ 399\u003c\/p\u003e \u003cp\u003e21.2.2 Definition and Properties of the ROC Curve \/ 400\u003c\/p\u003e \u003cp\u003e21.2.3 AUC and Degenerate ROC Curves \/ 401\u003c\/p\u003e \u003cp\u003e21.2.4 Overlapping Coefficient \/ 402\u003c\/p\u003e \u003cp\u003e21.2.5 Arrow Plot Construction \/ 403\u003c\/p\u003e \u003cp\u003e21.3 Significance Analysis of Microarrays \/ 404\u003c\/p\u003e \u003cp\u003e21.4 Correspondence Analysis \/ 405\u003c\/p\u003e \u003cp\u003e21.4.1 Basic Principles \/ 405\u003c\/p\u003e \u003cp\u003e21.4.2 Interpretation of CA Maps \/ 406\u003c\/p\u003e \u003cp\u003e21.5 Impact of the Preprocessing Methods \/ 407\u003c\/p\u003e \u003cp\u003e21.5.1 Class Prediction Context \/ 408\u003c\/p\u003e \u003cp\u003e21.5.2 Class Comparison Context \/ 408\u003c\/p\u003e \u003cp\u003e21.6 Conclusions \/ 412\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 413\u003c\/p\u003e \u003cp\u003eReferences \/ 413\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI PATTERN RECOGNITION IN PHYLOGENETIC TREES 417\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 PATTERN RECOGNITION IN PHYLOGENETICS: TREES AND NETWORKS 419\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDavid A. Morrison\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction \/ 419\u003c\/p\u003e \u003cp\u003e22.2 Networks and Trees \/ 420\u003c\/p\u003e \u003cp\u003e22.3 Patterns and Their Processes \/ 424\u003c\/p\u003e \u003cp\u003e22.4 The Types of Patterns \/ 427\u003c\/p\u003e \u003cp\u003e22.5 Fingerprints \/ 431\u003c\/p\u003e \u003cp\u003e22.6 Constructing Networks \/ 433\u003c\/p\u003e \u003cp\u003e22.7 Multi-Labeled Trees \/ 435\u003c\/p\u003e \u003cp\u003e22.8 Conclusion \/ 436\u003c\/p\u003e \u003cp\u003eReferences \/ 437\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 DIVERSE CONSIDERATIONS FOR SUCCESSFUL PHYLOGENETIC TREE RECONSTRUCTION: IMPACTS FROM MODEL MISSPECIFICATION, RECOMBINATION, HOMOPLASY, AND PATTERN RECOGNITION 439\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDiego Mallo, Agustín Sánchez-Cobos, and Miguel Arenas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction \/ 440\u003c\/p\u003e \u003cp\u003e23.2 Overview on Methods and Frameworks for Phylogenetic Tree Reconstruction \/ 440\u003c\/p\u003e \u003cp\u003e23.2.1 Inferring Gene Trees \/ 441\u003c\/p\u003e \u003cp\u003e23.2.2 Inferring Species Trees \/ 442\u003c\/p\u003e \u003cp\u003e23.3 Influence of Substitution Model Misspecification on Phylogenetic Tree Reconstruction \/ 445\u003c\/p\u003e \u003cp\u003e23.4 Influence of Recombination on Phylogenetic Tree Reconstruction \/ 446\u003c\/p\u003e \u003cp\u003e23.5 Influence of Diverse Evolutionary Processes on Species Tree Reconstruction \/ 447\u003c\/p\u003e \u003cp\u003e23.6 Influence of Homoplasy on Phylogenetic Tree Reconstruction: The Goals of Pattern Recognition \/ 449\u003c\/p\u003e \u003cp\u003e23.7 Concluding Remarks \/ 449\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 450\u003c\/p\u003e \u003cp\u003eReferences \/ 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 AUTOMATED PLAUSIBILITY ANALYSIS OF LARGE PHYLOGENIES 457\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDavid Dao, Tomáš Flouri, and Alexandros Stamatakis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction \/ 457\u003c\/p\u003e \u003cp\u003e24.2 Preliminaries \/ 459\u003c\/p\u003e \u003cp\u003e24.3 A Naïve Approach \/ 462\u003c\/p\u003e \u003cp\u003e24.4 Toward a Faster Method \/ 463\u003c\/p\u003e \u003cp\u003e24.5 Improved Algorithm \/ 467\u003c\/p\u003e \u003cp\u003e24.5.1 Preprocessing \/ 467\u003c\/p\u003e \u003cp\u003e24.5.2 Computing Lowest Common Ancestors \/ 468\u003c\/p\u003e \u003cp\u003e24.5.3 Constructing the Induced Tree \/ 468\u003c\/p\u003e \u003cp\u003e24.5.4 Final Remarks \/ 471\u003c\/p\u003e \u003cp\u003e24.6 Implementation \/ 473\u003c\/p\u003e \u003cp\u003e24.6.1 Preprocessing \/ 473\u003c\/p\u003e \u003cp\u003e24.6.2 Reconstruction \/ 473\u003c\/p\u003e \u003cp\u003e24.6.3 Extracting Bipartitions \/ 474\u003c\/p\u003e \u003cp\u003e24.7 Evaluation \/ 474\u003c\/p\u003e \u003cp\u003e24.7.1 Test Data Sets \/ 474\u003c\/p\u003e \u003cp\u003e24.7.2 Experimental Results \/ 475\u003c\/p\u003e \u003cp\u003e24.8 Conclusion \/ 479\u003c\/p\u003e \u003cp\u003eAcknowledgment \/ 481\u003c\/p\u003e \u003cp\u003eReferences \/ 481\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 A NEW FAST METHOD FOR DETECTING AND VALIDATING HORIZONTAL GENE TRANSFER EVENTS USING PHYLOGENETIC TREES AND AGGREGATION FUNCTIONS 483\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDunarel Badescu, Nadia Tahiri, and Vladimir Makarenkov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction \/ 483\u003c\/p\u003e \u003cp\u003e25.2 Methods \/ 485\u003c\/p\u003e \u003cp\u003e25.2.1 Clustering Using Variability Functions \/ 485\u003c\/p\u003e \u003cp\u003e25.2.2 Other Variants of Clustering Functions Implemented in the Algorithm \/ 487\u003c\/p\u003e \u003cp\u003e25.2.3 Description of the New Algorithm \/ 488\u003c\/p\u003e \u003cp\u003e25.2.4 Time Complexity \/ 491\u003c\/p\u003e \u003cp\u003e25.3 Experimental Study \/ 491\u003c\/p\u003e \u003cp\u003e25.3.1 Implementation \/ 491\u003c\/p\u003e \u003cp\u003e25.3.2 Synthetic Data \/ 491\u003c\/p\u003e \u003cp\u003e25.3.3 Real Prokaryotic (Genomic) Data \/ 495\u003c\/p\u003e \u003cp\u003e25.4 Results and Discussion \/ 501\u003c\/p\u003e \u003cp\u003e25.4.1 Analysis of Synthetic Data \/ 501\u003c\/p\u003e \u003cp\u003e25.4.2 Analysis of Prokaryotic Data \/ 502\u003c\/p\u003e \u003cp\u003e25.5 Conclusion \/ 502\u003c\/p\u003e \u003cp\u003eReferences \/ 503\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVII PATTERN RECOGNITION IN BIOLOGICAL NETWORKS 505\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 COMPUTATIONAL METHODS FOR MODELING BIOLOGICAL INTERACTION NETWORKS 507\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChristos Makris and Evangelos Theodoridis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction \/ 507\u003c\/p\u003e \u003cp\u003e26.2 Measures\/Metrics \/ 508\u003c\/p\u003e \u003cp\u003e26.3 Models of Biological Networks \/ 511\u003c\/p\u003e \u003cp\u003e26.4 Reconstructing and Partitioning Biological Networks \/ 511\u003c\/p\u003e \u003cp\u003e26.5 PPI Networks \/ 513\u003c\/p\u003e \u003cp\u003e26.6 Mining PPI Networks—Interaction Prediction \/ 517\u003c\/p\u003e \u003cp\u003e26.7 Conclusions \/ 519\u003c\/p\u003e \u003cp\u003eReferences \/ 519\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 BIOLOGICAL NETWORK INFERENCE AT MULTIPLE SCALES: FROM GENE REGULATION TO SPECIES INTERACTIONS 525\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAndrej Aderhold, V Anne Smith, and Dirk Husmeier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction \/ 525\u003c\/p\u003e \u003cp\u003e27.2 Molecular Systems \/ 528\u003c\/p\u003e \u003cp\u003e27.3 Ecological Systems \/ 528\u003c\/p\u003e \u003cp\u003e27.4 Models and Evaluation \/ 529\u003c\/p\u003e \u003cp\u003e27.4.1 Notations \/ 529\u003c\/p\u003e \u003cp\u003e27.4.2 Sparse Regression and the LASSO \/ 530\u003c\/p\u003e \u003cp\u003e27.4.3 Bayesian Regression \/ 530\u003c\/p\u003e \u003cp\u003e27.4.4 Evaluation Metric \/ 531\u003c\/p\u003e \u003cp\u003e27.5 Learning Gene Regulation Networks \/ 532\u003c\/p\u003e \u003cp\u003e27.5.1 Nonhomogeneous Bayesian Regression \/ 533\u003c\/p\u003e \u003cp\u003e27.5.2 Gradient Estimation \/ 534\u003c\/p\u003e \u003cp\u003e27.5.3 Simulated Bio-PEPA Data \/ 534\u003c\/p\u003e \u003cp\u003e27.5.4 Real mRNA Expression Profile Data \/ 535\u003c\/p\u003e \u003cp\u003e27.5.5 Method Evaluation and Learned Networks \/ 536\u003c\/p\u003e \u003cp\u003e27.6 Learning Species Interaction Networks \/ 540\u003c\/p\u003e \u003cp\u003e27.6.1 Regression Model of Species interactions \/ 540\u003c\/p\u003e \u003cp\u003e27.6.2 Multiple Global Change-Points \/ 541\u003c\/p\u003e \u003cp\u003e27.6.3 Mondrian Process Change-Points \/ 542\u003c\/p\u003e \u003cp\u003e27.6.4 Synthetic Data \/ 544\u003c\/p\u003e \u003cp\u003e27.6.5 Simulated Population Dynamics \/ 544\u003c\/p\u003e \u003cp\u003e27.6.6 Real World Plant Data \/ 546\u003c\/p\u003e \u003cp\u003e27.6.7 Method Evaluation and Learned Networks \/ 546\u003c\/p\u003e \u003cp\u003e27.7 Conclusion \/ 550\u003c\/p\u003e \u003cp\u003eReferences \/ 550\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 DISCOVERING CAUSAL PATTERNS WITH STRUCTURAL EQUATION MODELING: APPLICATION TO TOLL-LIKE RECEPTOR SIGNALING PATHWAY IN CHRONIC LYMPHOCYTIC LEUKEMIA 555\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAthina Tsanousa, Stavroula Ntoufa, Nikos Papakonstantinou, Kostas Stamatopoulos, and Lefteris Angelis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction \/ 555\u003c\/p\u003e \u003cp\u003e28.2 Toll-Like Receptors \/ 557\u003c\/p\u003e \u003cp\u003e28.2.1 Basics \/ 557\u003c\/p\u003e \u003cp\u003e28.2.2 Structure and Signaling of TLRs \/ 558\u003c\/p\u003e \u003cp\u003e28.2.3 TLR Signaling in Chronic Lymphocytic Leukemia \/ 559\u003c\/p\u003e \u003cp\u003e28.3 Structural Equation Modeling \/ 560\u003c\/p\u003e \u003cp\u003e28.3.1 Methodology of SEM Modeling \/ 560\u003c\/p\u003e \u003cp\u003e28.3.2 Assumptions \/ 561\u003c\/p\u003e \u003cp\u003e28.3.3 Estimation Methods \/ 562\u003c\/p\u003e \u003cp\u003e28.3.4 Missing Data \/ 562\u003c\/p\u003e \u003cp\u003e28.3.5 Goodness-of-Fit Indices \/ 563\u003c\/p\u003e \u003cp\u003e28.3.6 Other Indications of a Misspecified Model \/ 565\u003c\/p\u003e \u003cp\u003e28.4 Application \/ 566\u003c\/p\u003e \u003cp\u003e28.5 Conclusion \/ 580\u003c\/p\u003e \u003cp\u003eReferences \/ 581\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 ANNOTATING PROTEINS WITH INCOMPLETE LABEL INFORMATION 585\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGuoxian Yu, Huzefa Rangwala, and Carlotta Domeniconi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction \/ 585\u003c\/p\u003e \u003cp\u003e29.2 Related Work \/ 587\u003c\/p\u003e \u003cp\u003e29.3 Problem Formulation \/ 589\u003c\/p\u003e \u003cp\u003e29.3.1 The Algorithm \/ 591\u003c\/p\u003e \u003cp\u003e29.4 Experimental Setup \/ 592\u003c\/p\u003e \u003cp\u003e29.4.1 Data sets \/ 592\u003c\/p\u003e \u003cp\u003e29.4.2 Comparative Methods \/ 593\u003c\/p\u003e \u003cp\u003e29.4.3 Experimental Protocol \/ 594\u003c\/p\u003e \u003cp\u003e29.4.4 Evaluation Criteria \/ 594\u003c\/p\u003e \u003cp\u003e29.5 Experimental Analysis \/ 596\u003c\/p\u003e \u003cp\u003e29.5.1 Replenishing Missing Functions \/ 596\u003c\/p\u003e \u003cp\u003e29.5.2 Predicting Unlabeled Proteins \/ 600\u003c\/p\u003e \u003cp\u003e29.5.3 Component Analysis \/ 604\u003c\/p\u003e \u003cp\u003e29.5.4 Run Time Analysis \/ 604\u003c\/p\u003e \u003cp\u003e29.6 Conclusions \/ 605\u003c\/p\u003e \u003cp\u003eAcknowledgments \/ 606\u003c\/p\u003e \u003cp\u003eReferences \/ 606\u003c\/p\u003e \u003cp\u003eINDEX 609\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406941299031,"sku":"9781118893685","price":109.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118893685.jpg?v=1730497632"},{"product_id":"pattern-recognition-9781119302827","title":"Pattern Recognition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA new approach to the issue of data quality in pattern recognition\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDetailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal.\u003c\/p\u003e \u003cp\u003eFor decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been dataits sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. \u003ci\u003ePattern Recognition: A Quality of Data Pers\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003ePREFACE ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 1 FUNDAMENTALS 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1 PATTERN RECOGNITION: FEATURE SPACE CONSTRUCTION 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Concepts 3\u003c\/p\u003e \u003cp\u003e1.2 From Patterns to Features 8\u003c\/p\u003e \u003cp\u003e1.3 Features Scaling 17\u003c\/p\u003e \u003cp\u003e1.4 Evaluation and Selection of Features 23\u003c\/p\u003e \u003cp\u003e1.5 Conclusions 47\u003c\/p\u003e \u003cp\u003eAppendix 1.A 48\u003c\/p\u003e \u003cp\u003eAppendix 1.B 50\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2 PATTERN RECOGNITION: CLASSIFIERS 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Concepts 53\u003c\/p\u003e \u003cp\u003e2.2 Nearest Neighbors Classification Method 55\u003c\/p\u003e \u003cp\u003e2.3 Support Vector Machines Classification Algorithm 57\u003c\/p\u003e \u003cp\u003e2.4 Decision Trees in Classification Problems 65\u003c\/p\u003e \u003cp\u003e2.5 Ensemble Classifiers 78\u003c\/p\u003e \u003cp\u003e2.6 Bayes Classifiers 82\u003c\/p\u003e \u003cp\u003e2.7 Conclusions 97\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3 CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Concepts 102\u003c\/p\u003e \u003cp\u003e3.2 The Concept of Rejecting Architectures 107\u003c\/p\u003e \u003cp\u003e3.3 Native Patterns-Based Rejection 112\u003c\/p\u003e \u003cp\u003e3.4 Rejection Option in the Dataset of Native Patterns: A Case Study 118\u003c\/p\u003e \u003cp\u003e3.5 Conclusions 129\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4 EVALUATING PATTERN RECOGNITION PROBLEM 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Evaluating Recognition with Rejection: Basic Concepts 133\u003c\/p\u003e \u003cp\u003e4.2 Classification with Rejection with No Foreign Patterns 145\u003c\/p\u003e \u003cp\u003e4.3 Classification with Rejection: Local Characterization 149\u003c\/p\u003e \u003cp\u003e4.4 Conclusions 156\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5 RECOGNITION WITH REJECTION: EMPIRICAL ANALYSIS 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Experimental Results 160\u003c\/p\u003e \u003cp\u003e5.2 Geometrical Approach 175\u003c\/p\u003e \u003cp\u003e5.3 Conclusions 191\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 2 ADVANCED TOPICS: A FRAMEWORK OF GRANULAR COMPUTING 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6 CONCEPTS AND NOTIONS OF INFORMATION GRANULES 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Information Granularity and Granular Computing 197\u003c\/p\u003e \u003cp\u003e6.2 Formal Platforms of Information Granularity 201\u003c\/p\u003e \u003cp\u003e6.3 Intervals and Calculus of Intervals 205\u003c\/p\u003e \u003cp\u003e6.4 Calculus of Fuzzy Sets 208\u003c\/p\u003e \u003cp\u003e6.5 Characterization of Information Granules: Coverage and Specificity 216\u003c\/p\u003e \u003cp\u003e6.6 Matching Information Granules 219\u003c\/p\u003e \u003cp\u003e6.7 Conclusions 220\u003c\/p\u003e \u003cp\u003eReferences 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 7 INFORMATION GRANULES: FUNDAMENTAL CONSTRUCTS 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The Principle of Justifiable Granularity 223\u003c\/p\u003e \u003cp\u003e7.2 Information Granularity as a Design Asset 230\u003c\/p\u003e \u003cp\u003e7.3 Single-Step and Multistep Prediction of Temporal Data in Time Series Models 235\u003c\/p\u003e \u003cp\u003e7.4 Development of Granular Models of Higher Type 236\u003c\/p\u003e \u003cp\u003e7.5 Classification with Granular Patterns 241\u003c\/p\u003e \u003cp\u003e7.6 Conclusions 245\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8 CLUSTERING 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Fuzzy C-Means Clustering Method 247\u003c\/p\u003e \u003cp\u003e8.2 k-Means Clustering Algorithm 252\u003c\/p\u003e \u003cp\u003e8.3 Augmented Fuzzy Clustering with Clusters and Variables Weighting 253\u003c\/p\u003e \u003cp\u003e8.4 Knowledge-Based Clustering 254\u003c\/p\u003e \u003cp\u003e8.5 Quality of Clustering Results 254\u003c\/p\u003e \u003cp\u003e8.6 Information Granules and Interpretation of Clustering Results 256\u003c\/p\u003e \u003cp\u003e8.7 Hierarchical Clustering 258\u003c\/p\u003e \u003cp\u003e8.8 Information Granules in Privacy Problem: A Concept of Microaggregation 261\u003c\/p\u003e \u003cp\u003e8.9 Development of Information Granules of Higher Type 262\u003c\/p\u003e \u003cp\u003e8.10 Experimental Studies 264\u003c\/p\u003e \u003cp\u003e8.11 Conclusions 272\u003c\/p\u003e \u003cp\u003eReferences 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9 QUALITY OF DATA: IMPUTATION AND DATA BALANCING 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Data Imputation: Underlying Concepts and Key Problems 275\u003c\/p\u003e \u003cp\u003e9.2 Selected Categories of Imputation Methods 276\u003c\/p\u003e \u003cp\u003e9.3 Imputation with the Use of Information Granules 278\u003c\/p\u003e \u003cp\u003e9.4 Granular Imputation with the Principle of Justifiable Granularity 279\u003c\/p\u003e \u003cp\u003e9.5 Granular Imputation with Fuzzy Clustering 283\u003c\/p\u003e \u003cp\u003e9.6 Data Imputation in System Modeling 285\u003c\/p\u003e \u003cp\u003e9.7 Imbalanced Data and their Granular Characterization 286\u003c\/p\u003e \u003cp\u003e9.8 Conclusions 291\u003c\/p\u003e \u003cp\u003eReferences 291\u003c\/p\u003e \u003cp\u003eINDEX 293\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407031312727,"sku":"9781119302827","price":97.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119302827.jpg?v=1730497936"},{"product_id":"image-segmentation-principles-techniques-and-applications-9781119859000","title":"Image Segmentation  Principles Techniques and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field  The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture.  Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authorssuch as convolutional neural networks, graph convolutional networks, deformable convolution, and model compressionto assist graduate students and researchers apply and improve image segmentation in their work.  Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology.   Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory.   Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc.   Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc.    Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eAbout the Authors\u003c\/p\u003e \u003cp\u003eList of Abbreviations\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One: Principle \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1   Introduction to Image Segmentation\u003c\/p\u003e \u003cp\u003e2   Principles of Clustering\u003c\/p\u003e \u003cp\u003e3   Principles of Mathematical Morphology\u003c\/p\u003e \u003cp\u003e4   Principles of Neural Network\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two: Methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5   Fast and Robust Image Segmentation Using Clustering\u003c\/p\u003e \u003cp\u003e6   Fast Image Segmentation Using Watershed Transform\u003c\/p\u003e \u003cp\u003e7   Superpixel-based Fast Image Segmentation\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three:  Application\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8   Image Segmentation for Traffic Scene Analysis\u003c\/p\u003e \u003cp\u003e9   Image Segmentation for Medical Analysis\u003c\/p\u003e \u003cp\u003e10 Image Segmentation for Remote Sensing Analysis\u003c\/p\u003e \u003cp\u003e11 Image Segmentation for Material Analysis\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407175983447,"sku":"9781119859000","price":99.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119859000.jpg?v=1730498438"},{"product_id":"advanced-analytics-with-spark-9781491972953","title":"Advanced Analytics with Spark","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world datasets together to teach you how to approach analytics problems by example.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49409192427863,"sku":"9781491972953","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781491972953.jpg?v=1730505864"},{"product_id":"change-detection-and-image-time-series-analysis-2-supervised-methods-9781789450576","title":"Change Detection and Image Time Series Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChange Detection and Image Time Series Analysis 2\u003c\/i\u003e presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.\u003cbr\u003e\u003cbr\u003eChapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.\u003cbr\u003e\u003cbr\u003eChapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.\u003cbr\u003e\u003cbr\u003eChapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,\u003cbr\u003e\u003cbr\u003eChapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and\/or multilabel change classification issues.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE\u003c\/p\u003e \u003cp\u003eList of Notations\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIhsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO\u003c\/p\u003e \u003cp\u003eand Josiane ZERUBIA\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1. The role of multisensor data in time series classification 1\u003c\/p\u003e \u003cp\u003e1.1.2. Multisensor and multiresolution classification 2\u003c\/p\u003e \u003cp\u003e1.1.3.Previouswork 5\u003c\/p\u003e \u003cp\u003e1.2. Methodology 9\u003c\/p\u003e \u003cp\u003e1.2.1. Overview of the proposed approaches 9\u003c\/p\u003e \u003cp\u003e1.2.2. Hierarchical model associated with the first proposed method 10\u003c\/p\u003e \u003cp\u003e1.2.3. Hierarchical model associated with the second proposed method 13\u003c\/p\u003e \u003cp\u003e1.2.4. Multisensor hierarchical MPM inference 14\u003c\/p\u003e \u003cp\u003e1.2.5. Probability density estimation through finite mixtures 17\u003c\/p\u003e \u003cp\u003e1.3.Examplesofexperimentalresults 19\u003c\/p\u003e \u003cp\u003e1.3.1.Resultsofthefirstmethod 19\u003c\/p\u003e \u003cp\u003e1.3.2.Resultsofthesecondmethod 22\u003c\/p\u003e \u003cp\u003e1.4.Conclusion 26\u003c\/p\u003e \u003cp\u003exiii\u003c\/p\u003e \u003cp\u003e1.5.Acknowledgments 26\u003c\/p\u003e \u003cp\u003e1.6.References 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCharlotte PELLETIER and Silvia VALERO\u003c\/p\u003e \u003cp\u003e2.1. Introduction 33\u003c\/p\u003e \u003cp\u003e2.2. Basic concepts in supervised remote sensing classification 35\u003c\/p\u003e \u003cp\u003e2.2.1. Preparing data before it is fed into classification algorithms 35\u003c\/p\u003e \u003cp\u003e2.2.2. Key considerations when training supervised classifiers 39\u003c\/p\u003e \u003cp\u003e2.2.3. Performance evaluation of supervised classifiers 41\u003c\/p\u003e \u003cp\u003e2.3.Traditionalclassificationalgorithms 45\u003c\/p\u003e \u003cp\u003e2.3.1. Support vector machines 45\u003c\/p\u003e \u003cp\u003e2.3.2. Random forests 51\u003c\/p\u003e \u003cp\u003e2.3.3. k-nearest neighbor 56\u003c\/p\u003e \u003cp\u003e2.4. Classification strategies based on temporal feature representations 59\u003c\/p\u003e \u003cp\u003e2.4.1. Phenology-based classification approaches 60\u003c\/p\u003e \u003cp\u003e2.4.2 Dictionary-based classificationapproaches 61\u003c\/p\u003e \u003cp\u003e2.4.3 Shapelet-based classificationapproaches 62\u003c\/p\u003e \u003cp\u003e2.5.Deeplearningapproaches 63\u003c\/p\u003e \u003cp\u003e2.5.1. Introduction to deep learning 64\u003c\/p\u003e \u003cp\u003e2.5.2.Convolutionalneuralnetworks 68\u003c\/p\u003e \u003cp\u003e2.5.3.Recurrentneuralnetworks 71\u003c\/p\u003e \u003cp\u003e2.6.References 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Semantic Analysis of Satellite Image Time Series 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCorneliu Octavian DUMITRU and Mihai DATCU\u003c\/p\u003e \u003cp\u003e3.1. Introduction 85\u003c\/p\u003e \u003cp\u003e3.1.1.TypicalSITSexamples 89\u003c\/p\u003e \u003cp\u003e3.1.2. Irregular acquisitions 90\u003c\/p\u003e \u003cp\u003e3.1.3.Thechapterstructure 96\u003c\/p\u003e \u003cp\u003e3.2.WhyaresemanticsneededinSITS? 96\u003c\/p\u003e \u003cp\u003e3.3.Similaritymetrics 97\u003c\/p\u003e \u003cp\u003e3.4. Feature methods 98\u003c\/p\u003e \u003cp\u003e3.5. Classification methods 98\u003c\/p\u003e \u003cp\u003e3.5.1.Activelearning 99\u003c\/p\u003e \u003cp\u003e3.5.2.Relevancefeedback 100\u003c\/p\u003e \u003cp\u003e3.5.3. Compression-based pattern recognition 100\u003c\/p\u003e \u003cp\u003e3.5.4.LatentDirichletallocation 101\u003c\/p\u003e \u003cp\u003e3.6.Conclusion 102\u003c\/p\u003e \u003cp\u003evii\u003c\/p\u003e \u003cp\u003e3.7.Acknowledgments 105\u003c\/p\u003e \u003cp\u003e3.8.References 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMatthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU\u003c\/p\u003e \u003cp\u003e4.1. Introduction 109\u003c\/p\u003e \u003cp\u003e4.2. Annual time series 111\u003c\/p\u003e \u003cp\u003e4.2.1. Overview of annual time series methods 111\u003c\/p\u003e \u003cp\u003e4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112\u003c\/p\u003e \u003cp\u003e4.2.3.Towardsdensetimeseriesanalysis 116\u003c\/p\u003e \u003cp\u003e4.3. Dense time series analysis using all available data 117\u003c\/p\u003e \u003cp\u003e4.3.1. Making dense time series consistent 118\u003c\/p\u003e \u003cp\u003e4.3.2. Change detection methods 121\u003c\/p\u003e \u003cp\u003e4.3.3.Summaryandfuturedevelopments 125\u003c\/p\u003e \u003cp\u003e4.4. Deep learning-based time series analysis approaches 126\u003c\/p\u003e \u003cp\u003e4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129\u003c\/p\u003e \u003cp\u003e4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131\u003c\/p\u003e \u003cp\u003e4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134\u003c\/p\u003e \u003cp\u003e4.4.4. Synthesis and future developments 136\u003c\/p\u003e \u003cp\u003e4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136\u003c\/p\u003e \u003cp\u003e4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137\u003c\/p\u003e \u003cp\u003e4.5.2. Deep learning and computer vision as technology enablers 138\u003c\/p\u003e \u003cp\u003e4.5.3.Futuresteps 139\u003c\/p\u003e \u003cp\u003e4.6.References 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ\u003c\/p\u003e \u003cp\u003e5.1. Introduction 155\u003c\/p\u003e \u003cp\u003e5.1.1. Research methodology and statistics 159\u003c\/p\u003e \u003cp\u003e5.2. Satellite-based earthquake damage assessment 165\u003c\/p\u003e \u003cp\u003e5.3. Pre-processing of satellite images before damage assessment 167\u003c\/p\u003e \u003cp\u003e5.4. Multi-source image analysis 168\u003c\/p\u003e \u003cp\u003e5.5. Contextual feature mining for damage assessment 169\u003c\/p\u003e \u003cp\u003e5.5.1.Texturalfeatures 170\u003c\/p\u003e \u003cp\u003e5.5.2. Filter-based methods 173\u003c\/p\u003e \u003cp\u003e5.6. Multi-temporal image analysis for damage assessment 175\u003c\/p\u003e \u003cp\u003e5.6.1. Use of machine learning in damage assessment problem 176\u003c\/p\u003e \u003cp\u003e5.6.2. Rapid earthquake damage assessment 180\u003c\/p\u003e \u003cp\u003e5.7. Understanding damage following an earthquake using satellite-based SAR 181\u003c\/p\u003e \u003cp\u003e5.7.1. SAR fundamental parameters and acquisition vector 185\u003c\/p\u003e \u003cp\u003e5.7.2. Coherent methods for damage assessment 188\u003c\/p\u003e \u003cp\u003e5.7.3. Incoherent methods for damage assessment 192\u003c\/p\u003e \u003cp\u003e5.7.4. Post-earthquake-only SAR data-based damage assessment 195\u003c\/p\u003e \u003cp\u003e5.7.5 Combination of coherent and incoherent methods for damage assessment 196\u003c\/p\u003e \u003cp\u003e5.7.6.Summary 198\u003c\/p\u003e \u003cp\u003e5.8. Use of auxiliary data sources 200\u003c\/p\u003e \u003cp\u003e5.9.Damagegrades 200\u003c\/p\u003e \u003cp\u003e5.10.Conclusionanddiscussion 203\u003c\/p\u003e \u003cp\u003e5.11.References 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER\u003c\/p\u003e \u003cp\u003eand Emmanuel TROUVÉ\u003c\/p\u003e \u003cp\u003e6.1. Introduction 223\u003c\/p\u003e \u003cp\u003e6.2. Coarse- to fine-grained change of state dataset 225\u003c\/p\u003e \u003cp\u003e6.3. Deep transfer learning models for change of state classification 232\u003c\/p\u003e \u003cp\u003e6.3.1.Deeplearningmodellibrary 232\u003c\/p\u003e \u003cp\u003e6.3.2.GraphstructuresfortheCNNlibrary 234\u003c\/p\u003e \u003cp\u003e6.3.3. Dimensionalities of the learnables for the CNN library 236\u003c\/p\u003e \u003cp\u003e6.4.Changeofstateanalysis 237\u003c\/p\u003e \u003cp\u003e6.4.1 Transfer learning adaptations for the change of state classificationissues 238\u003c\/p\u003e \u003cp\u003e6.4.2.Experimentalresults 239\u003c\/p\u003e \u003cp\u003e6.5.Conclusion 243\u003c\/p\u003e \u003cp\u003e6.6.Acknowledgments 244\u003c\/p\u003e \u003cp\u003e6.7.References 244\u003c\/p\u003e \u003cp\u003eList of Authors 247\u003c\/p\u003e \u003cp\u003eIndex 249\u003c\/p\u003e \u003cp\u003eSummary of Volume 1 253\u003c\/p\u003e","brand":"ISTE Ltd","offers":[{"title":"Default Title","offer_id":49412637983063,"sku":"9781789450576","price":124.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789450576.jpg?v=1730517446"},{"product_id":"eyestrain-reduction-in-stereoscopy-9781848219984","title":"Eyestrain Reduction in Stereoscopy","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eStereoscopic processes are increasingly used in virtual reality and entertainment. This technology is interesting because it allows for a quick immersion of the user, especially in terms of depth perception and relief clues. However, these processes tend to cause stress on the visual system if used over a prolonged period of time, leading some to question the cause of side effects that these systems generate in their users, such as eye fatigue.\u003c\/p\u003e \u003cp\u003eThis book explores the mechanisms of depth perception with and without stereoscopy and discusses the indices which are involved in the depth perception. The author describes the techniques used to capture and retransmit stereoscopic images. The causes of eyestrain related to these images are then presented along with their consequences in the long and short term. The study of the causes of eyestrain forms the basis for an improvement in these processes in the hopes of developing mechanisms for easier virtual viewing.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAcknowledgments ix\u003c\/p\u003e \u003cp\u003eIntroduction xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Principles of Depth and Shape Perception 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. Function of the eye 1\u003c\/p\u003e \u003cp\u003e1.2. Depth perception without stereoscopy 2\u003c\/p\u003e \u003cp\u003e1.2.1. Monocular cues 2\u003c\/p\u003e \u003cp\u003e1.2.2. Proprioceptive cues 7\u003c\/p\u003e \u003cp\u003e1.3. Depth perception through stereoscopic vision 9\u003c\/p\u003e \u003cp\u003e1.4. Perception of inclinations and curves 10\u003c\/p\u003e \u003cp\u003e1.4.1. Perception of inclination and obliqueness 10\u003c\/p\u003e \u003cp\u003e1.4.2. Perception of curves 14\u003c\/p\u003e \u003cp\u003e1.5. Artificial stereoscopic vision 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Technological Elements 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Taking a picture 25\u003c\/p\u003e \u003cp\u003e2.2. Reproduction 26\u003c\/p\u003e \u003cp\u003e2.2.1. Colorimetric differentiation 27\u003c\/p\u003e \u003cp\u003e2.2.2. Differentiation by polarization 28\u003c\/p\u003e \u003cp\u003e2.2.3. Active glasses 30\u003c\/p\u003e \u003cp\u003e2.2.4. Auto-stereoscopic screens 31\u003c\/p\u003e \u003cp\u003e2.2.5. Virtual reality headsets 33\u003c\/p\u003e \u003cp\u003e2.3. Motion parallax restitution 34\u003c\/p\u003e \u003cp\u003e2.3.1. Pseudoscopic movement 34\u003c\/p\u003e \u003cp\u003e2.3.2. Correcting pseudoscopic movements 35\u003c\/p\u003e \u003cp\u003e2.3.3. Monoscopic motion parallax 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Causes of Visual Fatigue in Stereoscopic Vision 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Conflict between accommodation and convergence 41\u003c\/p\u003e \u003cp\u003e3.2. Too much depth 44\u003c\/p\u003e \u003cp\u003e3.3. High spatial frequencies 46\u003c\/p\u003e \u003cp\u003e3.3.1. Limits of fusion 49\u003c\/p\u003e \u003cp\u003e3.3.2. Comfort and high frequencies. 50\u003c\/p\u003e \u003cp\u003e3.4. High temporal frequency 52\u003c\/p\u003e \u003cp\u003e3.5. Conflicts with monoscopic cues 52\u003c\/p\u003e \u003cp\u003e3.6. Vertical disparities 53\u003c\/p\u003e \u003cp\u003e3.7. Improper device settings 55\u003c\/p\u003e \u003cp\u003e3.7.1. Quality of image and display 55\u003c\/p\u003e \u003cp\u003e3.7.2. Differences between left and right images 56\u003c\/p\u003e \u003cp\u003e3.7.3. Speed of correction of pseudoscopic movements 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Short- and Long-term Consequences 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1. Short-term effects 59\u003c\/p\u003e \u003cp\u003e4.1.1. Decreasing ease of accommodation 59\u003c\/p\u003e \u003cp\u003e4.1.2. Decrease in stereoscopic acuity 59\u003c\/p\u003e \u003cp\u003e4.1.3. Effects on the punctum proximum 61\u003c\/p\u003e \u003cp\u003e4.1.4. More subjective effects 61\u003c\/p\u003e \u003cp\u003e4.2. Long-term consequences 62\u003c\/p\u003e \u003cp\u003e4.2.1. Long-term effects on children 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Measuring Visual Fatigue 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1. Visual acuity 63\u003c\/p\u003e \u003cp\u003e5.1.1. Different possible measurements 64\u003c\/p\u003e \u003cp\u003e5.1.2. Optotypes 64\u003c\/p\u003e \u003cp\u003e5.2. Proximum accommodation function 65\u003c\/p\u003e \u003cp\u003e5.3. Ease of accommodation 66\u003c\/p\u003e \u003cp\u003e5.4. Stereoscopic acuity 67\u003c\/p\u003e \u003cp\u003e5.4.1. Tests of distance vision 67\u003c\/p\u003e \u003cp\u003e5.4.2. Tests of near vision 68\u003c\/p\u003e \u003cp\u003e5.5. Disassociated heterophorias 71\u003c\/p\u003e \u003cp\u003e5.6. Fusional reserves 72\u003c\/p\u003e \u003cp\u003e5.7. Subjective tests 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Reducing Spatial Frequencies 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1. Principle 75\u003c\/p\u003e \u003cp\u003e6.2. Technical solution 75\u003c\/p\u003e \u003cp\u003e6.2.1. Wavelets 76\u003c\/p\u003e \u003cp\u003e6.2.2. BOX FILTER 92\u003c\/p\u003e \u003cp\u003e6.2.3. Using a rolling average and other “blurs” 98\u003c\/p\u003e \u003cp\u003e6.2.4. Comparison of algorithms 103\u003c\/p\u003e \u003cp\u003e6.2.5. Chosen solution 114\u003c\/p\u003e \u003cp\u003e6.3. Experiment 116\u003c\/p\u003e \u003cp\u003e6.3.1. The task 116\u003c\/p\u003e \u003cp\u003e6.4. Measurements of fatigue taken 118\u003c\/p\u003e \u003cp\u003e6.4.1. Objective measurements 118\u003c\/p\u003e \u003cp\u003e6.4.2. Procedure 119\u003c\/p\u003e \u003cp\u003e6.4.3. The subjects 120\u003c\/p\u003e \u003cp\u003e6.5. Result 120\u003c\/p\u003e \u003cp\u003e6.5.1. Proximum accommodation function 120\u003c\/p\u003e \u003cp\u003e6.5.2. Ease of accommodation 121\u003c\/p\u003e \u003cp\u003e6.5.3. Stereoscopic acuity 122\u003c\/p\u003e \u003cp\u003e6.5.4. Effectiveness in execution of the task 122\u003c\/p\u003e \u003cp\u003e6.5.5. Subjective measurements 123\u003c\/p\u003e \u003cp\u003e6.5.6. Conclusions 124\u003c\/p\u003e \u003cp\u003e6.5.7. Discussion 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Reducing the Distance Between the Virtual Cameras 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1. Principle 131\u003c\/p\u003e \u003cp\u003e7.1.1. Usefulness of stereoscopy in depth perception 132\u003c\/p\u003e \u003cp\u003e7.1.2. The objects 133\u003c\/p\u003e \u003cp\u003e7.1.3. Hypothesis 142\u003c\/p\u003e \u003cp\u003e7.2. Experiment 142\u003c\/p\u003e \u003cp\u003e7.2.1. Tasks 142\u003c\/p\u003e \u003cp\u003e7.2.2. Experimental conditions 143\u003c\/p\u003e \u003cp\u003e7.2.3. Subjects 144\u003c\/p\u003e \u003cp\u003e7.2.4. Measurements 144\u003c\/p\u003e \u003cp\u003e7.3. Results 145\u003c\/p\u003e \u003cp\u003e7.3.1. Results for fatigue 145\u003c\/p\u003e \u003cp\u003e7.3.2. Perception results 147\u003c\/p\u003e \u003cp\u003e7.4. Discussion 152\u003c\/p\u003e \u003cp\u003e7.4.1. Influence on visual fatigue 152\u003c\/p\u003e \u003cp\u003e7.4.2. Influence on visual perception 153\u003c\/p\u003e \u003cp\u003eConclusion 155\u003c\/p\u003e \u003cp\u003eBibliography 157\u003c\/p\u003e \u003cp\u003eIndex 167\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413726044503,"sku":"9781848219984","price":125.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848219984.jpg?v=1730521177"},{"product_id":"biometric-identification-law-and-ethics-9783030902551","title":"Biometric Identification, Law and Ethics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book is open access. This book undertakes a multifaceted and integrated examination of biometric identification, including the current state of the technology, how it is being used, the key ethical issues, and the implications for law and regulation. The five chapters examine the main forms of contemporary biometrics–fingerprint recognition, facial recognition and DNA identification– as well the integration of biometric data with other forms of personal data, analyses key ethical concepts in play, including privacy, individual autonomy, collective responsibility, and joint ownership rights, and proposes a raft of principles to guide the regulation of biometrics in liberal democracies.\u003c\/p\u003eBiometric identification technology is developing rapidly and being implemented more widely, along with other forms of information technology. As products, services and communication moves online, digital identity and security is becoming more important. Biometric identification facilitates this transition. Citizens now use biometrics to access a smartphone or obtain a passport; law enforcement agencies use biometrics in association with CCTV to identify a terrorist in a crowd, or identify a suspect via their fingerprints or DNA; and companies use biometrics to identify their customers and employees. In some cases the use of biometrics is governed by law, in others the technology has developed and been implemented so quickly that, perhaps because it has been viewed as a valuable security enhancement, laws regulating its use have often not been updated to reflect new applications. However, the technology associated with biometrics raises significant ethical problems, including in relation to individual privacy, ownership of biometric data, dual use and, more generally, as is illustrated by the increasing use of biometrics in authoritarian states such as China, the potential for unregulated biometrics to undermine fundamental principles of liberal democracy. Resolving these ethical problems is a vital step towards more effective regulation.\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAcknowledgment\u003c\/p\u003e\u003cp\u003e1. The Rise of Biometric Identification, Fingerprints and Applied Ethics\u003c\/p\u003e\u003cp\u003e2. Facial Recognition and Privacy Rights\u003c\/p\u003e\u003cp\u003e3. DNA Identification, Joint Rights and Collective Responsibility\u003c\/p\u003e\u003cp\u003e4. Biometric and Non-Biometric Integration: Dual Use Dilemmas\u003c\/p\u003e\u003cp\u003e5. The Future of Biometrics and Liberal Democracy\u003c\/p\u003e\u003cp\u003eIndex\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415657783639,"sku":"9783030902551","price":23.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030902551.jpg?v=1730527681"},{"product_id":"pattern-recognition-and-image-analysis-10th-iberian-conference-ibpria-2022-aveiro-portugal-may-4-6-2022-proceedings-9783031048807","title":"Pattern Recognition and Image Analysis: 10th","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022, held in Aveiro, Portugal, in May 2022. \u003cp\u003eThe 54 papers accepted for these proceedings were carefully reviewed and selected from 72 submissions. They deal with document analysis; medical image processing; biometrics; pattern recognition and machine learning; computer vision; and other applications. \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eDOCUMENT ANALYSIS.- Test Sample Selection for Handwriting Recognition through Language Modeling.- Classification of Untranscribed Handwritten Notarial Documents by Textual Contents.- Incremental Vocabularies in Machine Translation through Aligned Embedding Projections.- An Interactive Machine Translation Framework for Modernizing the Language of Historical Documents.- From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation.- MEDICAL IMAGE PROCESSING.- Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata.- Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection.- Deep Learning for Diagnosis of Alzheimer’s Disease with FDG-PET Neuroimaging.- Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes.- Increased Robustness in Chest X-ray Classification through Clinical Report-driven regularization.- MEDICAL APPLICATIONS.- Deep Detection Models for Measuring Epidermal Bladder Cells.- On the performance of deep learning models for respiratory sound classification trained on unbalanced data.- Automated Adequacy Assessment of Cervical Cytology Samples using Deep Learning.- Exploring Alterations in Electrocardiogram during the Postoperative Pain.- Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification.- Detection of epilepsy in EEGs using Deep Sequence models - A Comparative Study.- BIOMETRICS.- Facial Emotion Recognition for Sentiment Analysis of Social Media Data.- Heartbeat selection based on outlier removal.- Characterization of emotions through facial Electromyogram signals.- Feature selection for emotional well-being monitorization.- Temporal Convolutional Networks for Robust Face Liveness Detection.- PATTERN RECOGNITION \u0026amp; MACHINE LEARNING.- MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks.- Transparent management of adjacencies in the cubic grid.- Abbreviating Labelling Cost for Sentinel-2 Image Scene Classification through Active Learning.- Feature-based classification of archaeal sequences using compression-based methods.- A first approach to Image Transformation Sequence Retrieval.- Discriminative Learning of Two-Dimensional Probabilistic Context-Free Grammars for Mathematical Expression Recognition and Retrieval.- COMPUTER VISION.- Golf Swing Sequencing using Computer Vision.- Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition.- An Innovative Vision System for Floor-Cleaning Robots based on YOLOv5.- LIDAR Signature based Node Detection and Classification in graph topological maps for indoor navigation.- Event Vision in Egocentric Human Action Recognition.- An edge-based computer vision approach for determination of sulfonamides in water.- IMAGE PROCESSING.- Visual Semantic Context Encoding for Aerial Data Introspection and Domain Prediction.- An End-to-End Approach for Seam Carving Detection using Deep Neural Networks.- Proposal of a comparative framework for face super-resolution algorithms in forensics.- On the use of Transformers for end-to-end Optical Music Recognition.- Retrieval of Music-Notation Primitives via Image-to-Sequence.- Digital image conspicuous features classification using TLCNN model with SVM classifier.- Contribution of low, mid and high-level image features in predicting human similarity judgements.- On the Topological Disparity Characterization of Square-pixel Binary Image Data by a Labeled Bipartite Graph.- Learning Sparse Masks for Diffusion-based Image Inpainting.- Extracting Descriptive Words from Untranscribed Handwritten Images.- OTHER APPLICATIONS.- GMM-aided DNN Bearing Fault Diagnosis using Sparse Autoencoder Feature Extraction.- Identification of External Defects on FruitsUsing Deep Learning.- Improving Action Quality Assessment using Weighted Aggregation.- Improving Licence Plate Detection using Generative Adversarial Networks.- Film shot type classification based on camera movement styles.- The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition.- A case of study on traffic cone detection for autonomous racing on a Jetson platform.- Energy savings in residential buildings based on adaptive thermal comfort models.- Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition.- Using bus tracking data to detect potential hazard driving zones.- Dynamic PCA based statistical monitoring of air pollutant concentrations in wildfire scenarios.\u003cp\u003e \u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415676330327,"sku":"9783031048807","price":80.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031048807.jpg?v=1730527749"},{"product_id":"biometric-recognition-16th-chinese-conference-ccbr-2022-beijing-china-november-11-13-2022-proceedings-9783031202322","title":"Biometric Recognition: 16th Chinese Conference,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the proceedings of the 16th Chinese Conference on Biometric Recognition, CCBR 2022, which took place in Beijing, China, in November 2022.\u003cbr\u003eThe 70 papers presented in this volume were carefully reviewed and selected from 115 submissions. The papers cover a wide range of topics such as Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Gesture and Action Recognition; Affective Computing and Human-Computer Interface; Speaker and Speech Recognition; Gait, Iris and Other Biometrics; Multi-modal Biometric Recognition and Fusion; Quality Evaluation and Enhancement of Biometric Signals; Animal Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eFingerprint, Palmprint and Vein Recognition.- \u003c\/b\u003eA Finger BiModal Fusion Algorithm based on Improved DenseNet.- A lightweight segmentation network based on extraction.- A novel multi-layered minutiae extractor based on OCT fingerprints.- An overview and forecast of biometric recognition technology used in forensic science.- Combining Band-Limited OTSDF Filter and Directional Representation for Palmprint Recognition.- Cross-Dataset Image Matching Network for Heterogeneous Palmprint Recognition.- DUAL MODE NEAR-INFRARED SCANNER FOR IMAGING DORSAL HAND VEINS.- Multi-Stream Convolutional Neural Networks Fusion for Palmprint Recognition.- Multi-view Finger Vein Recognition using Attention-based MVCNN.- SELECTIVE DETAIL ENHANCEMENT ALGORITHM FOR FINGER VEIN IMAGES.- SP-FVR: SuperPoint-based Finger Vein Recognition.- TransFinger: Transformer based Finger Tri-modal Biometrics.- \u003cb\u003eFace Detection, Recognition and Tracking.- \u003c\/b\u003eA Survey of Domain Generalization-based Face Anti-spoofing.- An Empirical Comparative Analysis of Africans with Asians using DCNN Facial Biometric Models.- Disentanglement of Deep Features for Adversarial Face Detection.- Estimation of Gaze-Following Based on Transformer and the Guiding Offset.- Learning Optimal Transport Mapping of Joint Distribution for Cross-Scenario Face Anti-Spooffing.- MLFW: A Database for Face Recognition on Masked Faces.- Multi-scale object detection algorithm based on adaptive feature fusion.- Sparsity-Regularized Geometric Mean Metric Learning for Kinship Verification.- YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing.- \u003cb\u003eGesture and Action Recognition.- \u003c\/b\u003eAdaptive Joint Interdependency Learning for 2D Occluded Hand Pose Estimation.- Contrastive and Consistent Learning for Unsupervised Human Parsing.- Dynamic Hand Gesture Authentication Based on Improved Two-stream CNN.- Efficient Video Understanding-based Random Hand Gesture Authentication.- Multidimension Joint Networks for Action Recognition.- Multi-Level Temporal-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition.- Research on Gesture Recognition of Surface EMG Based on Machine Learning.- \u003cb\u003eAffective Computing and Human-Computer Interface.- \u003c\/b\u003eAdaptive Enhanced Micro-expression Spotting Network based on Multi-stage Features Extraction.- Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition.- Hemispheric Asymmetry Measurement Network for Emotion Classification.- Human Action Recognition Algorithm of Non-Local Two-Stream Convolution Network Based on Image Depth Flow.- Synthetic Feature Generative Adversarial Network for Motor Imagery Classification: Create Feature from Sampled Data.- \u003cb\u003eSpeaker and Speech Recognition.- \u003c\/b\u003eAn End-to-end Conformer-based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation.- ATRemix: An Auto-Tune Remix Dataset for Singer Recognition.- Low-resource speech keyword search based on residual neural network.- Online Neural Speaker Diarization with Core Samples.- Pose-unconstrainted 3D Lip Behaviometrics via Unsupervised Symmetry Correction.- Virtual Fully-Connected Layer for a Large-Scale Speaker Verification Dataset.- \u003cb\u003eGait, Iris and Other Biometrics.- \u003c\/b\u003eA Simple Convolutional Neural Network for Small Sample Multi-lingual Offline Handwritten Signature Recognition.- Attention Skip Connection Dense Network for Accurate Iris Segmentation.- Gait Recognition with Various Data Modalities: A Review.- INCREMENTAL EEG BIOMETRIC RECOGNITION BASED ON EEG RELATION NETWORK.- Salient Foreground-Aware Network for Person Search.- Shoe print retrieval algorithm based on improved ecientnetV2.- \u003cb\u003eMulti-modal Biometric Recognition and Fusion.- \u003c\/b\u003eA novel dual-modal biometric recognition method based on weighted joint group sparse representation classification.- FINGER TRIMODAL FEATURES CODING FUSION METHOD.- Fusion of Gait and Face for Human Identification at the Feature Level.- Gait Recognition in Sensing Insoles: a study based on a Hybrid CNN-Attention-LSTM Network.- Identity Authentication Using a Multimodal Sensing Insole a Feasibility Study.- MDF-Net: Multimodal Deep Fusion for Large-scale Product Recognition.- Survey on Deep Learning based Fusion Recognition of Multimodal Biometrics.- Synthesizing Talking Face Videos with a Spatial Attention Mechanism.- \u003cb\u003eQuality Evaluation and Enhancement of Biometric Signals.- \u003c\/b\u003eBlind Perceptual Quality Assessment for Single Image Motion Deblurring.- Low-illumination Palmprint Image Enhancement Method Based On  U-Net Neural Network.- Texture-guided multiscale feature learning network for palmprint image quality assessment.- \u003cb\u003eAnimal Biometrics.- \u003c\/b\u003eAn Adaptive Weight Joint Loss Optimization For Dog Face Recognition.- Improved YOLOv5 for Dense Wildlife Object Detection\u003cb\u003e.- \u003c\/b\u003eSelf-Attention based Cross-level Fusion Network for Camou aged Object Detection.- \u003cb\u003eTrustyworth, Privacy and Persondal Data Security.- \u003c\/b\u003eFace Forgery Detection by Multi-dimensional Image Decomposition.- IrisGuard: Image Forgery Detection for Iris Anti-spooffing.- Multi-branch network with circle loss using voice conversion and channel robust data augmentation for synthetic speech detection.- Spoof Speech Detection Based on Raw Cross-dimension Interaction Attention Network.- \u003cb\u003eMedical and Other Applications.-  \u003c\/b\u003eA Deformable Convolution Encoder with Multi-Scale Attention Fusion Mechanism for Classification of Brain Tumor MRI Images.- GI Tract Lesion Classification Using Multi-task Capsule Networks with Hierarchical Convolutional Layers.- Grading Diagnosis of Sacroiliitis in CT Scans Based on Radiomics and Deep Learning.- Noninvasive blood pressure waveform measurement method based on CNN-LSTM.- Recurrence Quantification Analysis of Cardiovascular System During Cardiopulmonary Resuscitation.- UAV AERIAL PHOTOGRAPHY TRAFFIC OBJECT DETECTION BASED ON LIGHTWEIGHT DESIGN AND FEATURE FUSION.- UMixer: A novel U-shaped convolutional mixer for multi-scale feature fusion in Medical Image Segmentation.\u003c\/p\u003e\u003cbr\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415696449879,"sku":"9783031202322","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031202322.jpg?v=1730527819"},{"product_id":"recent-trends-in-image-processing-and-pattern-recognition-5th-international-conference-rtip2r-2022-kingsville-tx-usa-december-1-2-2022-revised-selected-papers-9783031235986","title":"Recent Trends in Image Processing and Pattern","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022, held in Kingsville, TX, USA, in collaboration with the Applied AI Research Laboratory of the University of South Dakota, during December 01-02, 2022.\u003cbr\u003eThe 31 full papers included in this book were carefully reviewed and selected from 69 submissions. They were organized in topical sections as follows: healthcare: medical imaging and informatics; computer vision and pattern recognition; internet of things and security; and signal processing and machine learning.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e​\u003cb\u003eHealthcare: medical imaging and informatics\u003c\/b\u003e.- Data Characterization for Reliable AI in Medicine.- Alzheimer’s Disease Detection using Ensemble Learning and Artificial Neural Networks.- Semi-supervised Multi-domain Learning for Medical Image Classification.- Significant CC400 functional brain parcellations based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder detection.- 2D respiratory sound analysis to detect lung abnormalities.- Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality due to Infectious Disease.- Chest X-ray Image Super-resolution via Deep Contrast Consistent Feature Network.- A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images.- Federated Learning for Lung Sound Analysis.- Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification using Thermal Image.- Image Processing and Pattern Recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood.- An Extreme Learning Machine-basedAutoEncoder (ELM-AE)for denoising knee X-ray images and grading knee osteoarthritis severity.- \u003cb\u003eComputer Vision and Pattern Recognition\u003c\/b\u003e.- Motor Imagery Classification CombiningRiemannian Geometry and Artificial Neural Networks.- Autism Spectrum Disorder Detection using Transfer Learning with VGG 19, Inception V3 and DenseNet 201.- Shrimp Shape Analysis by a Chord LengthFunction Based Methodology.- Supervised Neural Networks for Fruit Identification.- Targeted Clean-Label Poisoning Attacks On Federated Learning.- Building Marathi SentiWordNet.- A computational study on calibrated VGG19 formultimodal learning and representation insurveillance.- Automated Deep Learning based approach for Albinism Detection.- A Deep learning-based regression scheme for angle estimation in image dataset.- The classification of Native and Invasive Speciesin North America: A Transfer Learning and Random Forest Pipeline.- \u003cb\u003eInternet of Things and Security\u003c\/b\u003e.- Towards a Digital Twin Integrated DLT and IoT-based Automated Healthcare Ecosystem.- Enabling Edge Devices using Federated Learning and Big Data for Proactive Decisions.- IoT and Blockchain oriented gender determination of Bangladeshi populations.- Federated Learning based secured computational offloading in cyber-physical IoST systems.- A Hybrid Campus Security System Combined ofFace, Number-plate, and Voice Recognition.- \u003cb\u003eSignal Processing and Machine\u003c\/b\u003e.- Single-trial detection of event-related potentials with artificial examples based on coloring transformation.- Identifying the relationship between hypothesis and premise.- Data Poisoning Attack by Label Flipping onSplitFed Learning.- A Deep Learning-powered voice-enabled mathtutor for kids.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415700349271,"sku":"9783031235986","price":71.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031235986.jpg?v=1730527834"},{"product_id":"computer-vision-eccv-2022-workshops-tel-aviv-israel-october-23-27-2022-proceedings-part-i-9783031250552","title":"Computer Vision – ECCV 2022 Workshops: Tel Aviv,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.\u003cp\u003eThe 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows:\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart II:\u003c\/b\u003e W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart III:\u003c\/b\u003e W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart V:\u003c\/b\u003e W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VI\u003c\/b\u003e: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VII:\u003c\/b\u003e W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology\/COVID19; W31 - Compositional and Multimodal Perception;\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VIII:\u003c\/b\u003e W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415701496151,"sku":"9783031250552","price":80.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031250552.jpg?v=1730527838"},{"product_id":"computer-vision-eccv-2022-workshops-tel-aviv-israel-october-23-27-2022-proceedings-part-iv-9783031250682","title":"Computer Vision – ECCV 2022 Workshops: Tel Aviv,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.\u003cp\u003eThe 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows:\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart II:\u003c\/b\u003e W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart III:\u003c\/b\u003e W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart V:\u003c\/b\u003e W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VI\u003c\/b\u003e: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VII:\u003c\/b\u003e W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology\/COVID19; W31 - Compositional and Multimodal Perception;\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VIII:\u003c\/b\u003e W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415701594455,"sku":"9783031250682","price":80.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031250682.jpg?v=1730527838"},{"product_id":"computer-vision-eccv-2022-workshops-tel-aviv-israel-october-23-27-2022-proceedings-part-viii-9783031250842","title":"Computer Vision – ECCV 2022 Workshops: Tel Aviv,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.\u003cp\u003eThe 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows:\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart II:\u003c\/b\u003e W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart III:\u003c\/b\u003e W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart V:\u003c\/b\u003e W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VI\u003c\/b\u003e: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VII:\u003c\/b\u003e W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology\/COVID19; W31 - Compositional and Multimodal Perception;\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VIII:\u003c\/b\u003e W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415701627223,"sku":"9783031250842","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031250842.jpg?v=1730527838"},{"product_id":"computer-vision-eccv-2022-workshops-tel-aviv-israel-october-23-27-2022-proceedings-part-vii-9783031250811","title":"Computer Vision – ECCV 2022 Workshops: Tel Aviv,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.\u003cp\u003eThe 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows:\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart I:\u003c\/b\u003e W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart II:\u003c\/b\u003e W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart III:\u003c\/b\u003e W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IV:\u003c\/b\u003e W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart V:\u003c\/b\u003e W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VI\u003c\/b\u003e: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning;\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VII:\u003c\/b\u003e W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology\/COVID19; W31 - Compositional and Multimodal Perception;\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart VIII:\u003c\/b\u003e W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415701692759,"sku":"9783031250811","price":80.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031250811.jpg?v=1730527839"},{"product_id":"neural-information-processing-29th-international-conference-iconip-2022-virtual-event-november-22-26-2022-proceedings-part-i-9783031301049","title":"Neural Information Processing: 29th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.\u003cbr\u003eThe 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.\u003cbr\u003eThe ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eTheory and Algorithms.- \u003c\/b\u003eSolving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.-  Multi-Agent Hyper-Attention Policy Optimization.-  Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.-  Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata:  Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.-  A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415706607959,"sku":"9783031301049","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031301049.jpg?v=1730527854"},{"product_id":"image-analysis-22nd-scandinavian-conference-scia-2023-sirkka-finland-april-18-21-2023-proceedings-part-i-9783031314346","title":"Image Analysis: 22nd Scandinavian Conference,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023.\u003cbr\u003e\u003cbr\u003eThe 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and\/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eDatasets and Evaluation.- Action and Behaviour Recognition.- Image and Video Processing, Analysis, and Understanding.- Detection, Recognition, Classification, and Localization in 2D and\/or 3D.- Machine Learning and Deep Learning.\u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415707984215,"sku":"9783031314346","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031314346.jpg?v=1730527858"},{"product_id":"pattern-recognition-15th-mexican-conference-mcpr-2023-tepic-mexico-june-21-24-2023-proceedings-9783031337826","title":"Pattern Recognition: 15th Mexican Conference,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 15th Mexican Conference on Pattern Recognition, MCPR 2023, held in Tepic, Mexico, during June 21–24, 2023.\u003cbr\u003eThe 30 full papers presented in this book were carefully reviewed and selected from 61 submissions. The papers are divided into the following topical sections: pattern recognition and machine learning techniques; deep learning and neural networks; medical applications of pattern recognition; language processing and recognition; and industrial applications of pattern recognition.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePattern Recognition and Machine Learning \u003c\/b\u003e\u003cb\u003eTechniques: \u003c\/b\u003eFeature Analysis and Selection for Water Stream Modeling.- A Cloud-based (AWS) Machine Learning Solution to Predict Account Receivables in a Financial Institution.- A New Approach for Road Type Classification using Multi-Stage Graph Embedding Method.- Removing the Black-Box from Machine Learning.- Using Machine Learning to Identify Patterns in Learner-Submitted Code for the Purpose of Assessment.- Fitness Function Comparison for Unsupervised Feature Selection with Permutational-Based Dierential Evolution.- A Method for Counting Models on Cubic Boolean Formulas.- Automatic Identication of Learning Styles through Behavioral Patterns.- Comparison of Classiers in Challenge Scheme.- \u003cb\u003eDeep Learning and Neural Networks: \u003c\/b\u003eRobust Zero-Watermarking for Medical Images based on Deep Learning Feature Extraction.- Plant Stress Recognition Using Deep Learning and 3D Reconstruction.- Segmentation and Classification Networks for Corn\/Weed Detection under Excessive Field Variabilities.- Leukocyte Recognition Using a Modified AlexNet and Image to Image GAN Data Augmentation.- Spoofing Detection for Speaker Verification with Glottal Flow and 1D Pure Convolutional Networks.- Estimation of Stokes Parameters using Deep Neural Networks.- Experimental Study of the Performance of Convolutional Neural Networks Applied in Art Media Classification.- \u003cb\u003eMedical Applications of Pattern Recognition: \u003c\/b\u003eHadamard Layer to Improve Semantic Segmentation in Medical Images.- Patterns in Genesis of Breast Cancer Tumor.- Realistic Simulation of Event-Related Potentials and their usual Noise and Interferences for Pattern Recognition.- Chest X-ray Imaging Severity Score of COVID-19 Pneumonia.- Leukocyte Detection with Novel Fully Convolutional Network and a New Dataset of Blood Smear Complete Samples.- Comparison of Deep Learning Architectures in Classification of Microcalcifications Clusters in Digital Mammograms.- Retinal Artery and Vein Segmentation using an Image-to-image Conditional Adversarial Network.- Evaluation of Heatmaps as an Explicative Method for Classifying Acute Lymphoblastic Leukemia Cells.- \u003cb\u003eLanguage Processing and Recognition: \u003c\/b\u003eMachine Learning Models Applied in Sign Language Recognition.- Urdu Semantic Parsing: An Improved SEMPRE Framework for Conversion of Urdu Language Web Queries to Logical forms.- Improving the Identification of Abusive Language through Careful Design of Pre-training Tasks.- \u003cb\u003eIndustrial Applications of Pattern Recognition: \u003c\/b\u003eTOPSIS Method for Multiple-Criteria Decision-Making Applied to Trajectory Selection for Autonomous Driving.- Machine-learning based Estimation of the Bending Magnitude Sensed by a Fiber Optic Device.- Graph-based Semi-Supervised Learning using Riemannian Geometry Distance for Motor Imagery Classification.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415710507351,"sku":"9783031337826","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031337826.jpg?v=1730527866"},{"product_id":"ophthalmic-medical-image-analysis-10th-international-workshop-omia-2023-held-in-conjunction-with-miccai-2023-vancouver-bc-canada-october-12-2023-proceedings-9783031440120","title":"Ophthalmic Medical Image Analysis: 10th","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the refereed proceedings of the 10th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2023, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023.\u003cbr\u003e\u003cp\u003eThe 16 papers presented at OMIA 2023 were carefully reviewed and selected from 27 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415722533207,"sku":"9783031440120","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031440120.jpg?v=1730527897"},{"product_id":"advanced-concepts-for-intelligent-vision-systems-21st-international-conference-acivs-2023-kumamoto-japan-august-21-23-2023-proceedings-9783031453816","title":"Advanced Concepts for Intelligent Vision Systems:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the proceedings of the 21st International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2023, held in Kumamoto, Japan, during August 2023. \u003cbr\u003eThe 31 papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named: Computer Vision, Affective Computing and Human Interactions, Managing the Biodiversity, Robotics and Drones, Machine Learning.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eA hybrid quantum-classical segment-based Stereo Matching algorithm.- Continuous Exposure for Extreme Low-Light Imaging.- Semi-supervised Classification and Segmentation of Forest Fire using Autoencoders.- Descriptive and coherent paragraph generation for image paragraph captioning using vision transformer and post-processing.- Pyramid Swin Transformer for Multi-Task: Expanding to more computer vision tasks.- Person activity classification from an aerial sensor based on a multi-level deep features.- Person Quick-Search Approach based on a Facial Semantic Attributes Description.- Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers.- BENet: A lightweight bottom-up framework for context-aware emotion recognition.- Yolopoint: Joint Keypoint and Object Detection.- Less-than-one shot 3d segmentation hijacking a pre-trained space-time memory network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- A Single Image Neuro-Geometric Depth Estimation.- Wave-shaping Neural Activation for Improved 3D Model Reconstruction from Sparse Point Clouds.- A Deep Learning Approach to Segment High-Content Images of the E.coli Bacteria.- Multimodal Emotion Recognition System Through Three Different Channels (MER-3C).- Multi-Modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets.- A Contrario Mosaic Analysis for Image Forensics.- IRIS SEGMENTATION TECHNIQUE USING IRIS-UNet METHOD.- Image Acquisition by Image Retrieval with Color Aesthetics.- Improved Obstructed Facial Feature Reconstruction for Emotion Recognition with Minimal Change CycleGANs.- Quality assessment for high dynamic range stereoscopic omnidirectional image system.- Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging.- Reinforcement Learning for truck Eco-driving: a serious game as driving assistance system.- Underwater mussel segmentation using smoothed shape descriptors with random forest.- A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain simulation.- Construction of a novel data set for pedestrian tree species detection using google street view data.- Texture-based Data Augmentation for Small Datasets.- Multimodal Representations for Teacher-Guided Compositional Visual Reasoning.- Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415724892503,"sku":"9783031453816","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031453816.jpg?v=1730527904"},{"product_id":"image-and-graphics-12th-international-conference-icig-2023-nanjing-china-september-22-24-2023-proceedings-part-ii-9783031463075","title":"Image and Graphics: 12th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.\u003cbr\u003eThe 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.\u003cbr\u003eThe ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high quality peer reviewed oral and poster presentations.\u003cbr\u003e \u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e​Computer Vision and Pattern Recognition:\u003c\/b\u003e Temporal Global Re-Detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking.- VLNet: A Multi-task Network for Joint Vehicle and Lane Detection.- Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-View Stereo.- A Novel Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Visual Attention Network.- Efficient Few-shot Image Generation via Lightweight Octave Generative Adversarial Networks.- Incorporating Global Correlation and Local Aggregation for Efficient Visual Localization.- Deep Interactive Image Semantic and Instance Segmentation.- Learning High-Performance Spiking Neural Networks With Multi-Compartment Spiking Neurons.- Attribute Space Analysis for Image Editing.-SAGAN: Self-Attention Generative Adversarial Net-work for RGB-D Saliency Prediction.- Behavioural State Detection Algorithm for Infants and Toddlers Incorporating Multi-scale Contextual Features.- Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark.- Dual Fusion Network for Hyperspectral Semantic Segmentation.- Strip-FFT Transformer for single image deblurring.- Vision-Language Adaptive Mutual Decoder for OOV-STR.- DensityLayout: Density-conditioned Layout GAN for Visual-textual Presentation Designs.- GLTCM: Global-Local Temporal and Cross-Modal  Network for Audio-Visual Event Localization.- Recent Advances in Class-Incremental Learning.- TTA-GCN: Temporal Topology Aggregation for Skeleton-Based Action Recognition.- A Stable Long-Term Tracking Method For Group-Housed Pigs.- Dynamic Attention for Isolated Sign Language Recognition with Reinforcement Learning.- A Segmentation Method based on SE Attention and U-Net for Apple Image.- Human Action Recognition Method based on spatio-temporal relationship.- Facial expression recognition from occluded images using deep convolution neural network with Vision Transformer.- Learning Discriminative Proposal Representation for Multi-Object Tracking.- Virtual-Violence: A Brand-New Dataset for Video Violence Recognition.- A novel Attention-DeblurGAN-Based Defogging Algorithm.- Multi-Modal Context-Aware Network for Scene Graph Generation.- VQA-CLPR: Turning a Visual Question Answering Model into a Chinese License Plate Recognizer.- Unsupervised Vehicle Re-Identification via Raw UAV Videos.- Distance-Aware Vector-Field and Vector Screening Strategy for 6D Object Pose Estimation.- SSTA-Net: Self-supervised Spatio-Temporal Attention Network for Action Recognition.- Gesture recognition method based on Sim-ConvNeXt model.- Research on Airborne Infrared Target Recognition Method based on Target-Environment Coupling.- Semantic and Gradient Guided Scene Text Image Super-Resolution.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415725973847,"sku":"9783031463075","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031463075.jpg?v=1730527908"}],"url":"https:\/\/bookcurl.com\/collections\/pattern-recognition.oembed?page=5","provider":"Book Curl","version":"1.0","type":"link"}