Pattern recognition Books
Cambridge University Press Introduction to Online Control
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£52.20
Elsevier Science Computer Vision
Book SynopsisTable of Contents1. Vision, the Challenge2. Images and Imaging Operations3. Image Filtering and Morphology4. The Role of Thresholding5. Edge Detection6. Corner, Interest Point and Invariant Feature Detection7. Texture Analysis8. Binary Shape Analysis9. Boundary Pattern Analysis10. Line, Circle and Ellipse Detection11. The Generalised Hough Transform12. Object Segmentation and Shape Models13. Basic Classification Concepts14. Machine Learning: Probabilistic Methods15. Deep Learning Networks16. The Three-Dimensional World17. Tackling the Perspective n-point Problem18. Invariants and perspective19. Image transformations and camera calibration20. Motion21. Face Detection and Recognition: the Impact of Deep Learning22. Surveillance23. In-Vehicle Vision Systems24. Epilogue—Perspectives in VisionAppendix A: Robust statisticsAppendix B: The Sampling TheoremAppendix C: The representation of colourAppendix D: Sampling from distributions
£77.39
Cambridge University Press Accelerating Deep Neural Networks
£37.05
Springer International Publishing AG Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent
With 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. This 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. This 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.
£80.99
Pearson Education (US) Quick Start Guide to Large Language Models
Book SynopsisSinan Ozdemir 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.Trade Review"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."--Shelia Gulati, former GM at Microsoft and current Managing Director of Tola Capital "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. "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. "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. "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."--Pedro Marcelino, Machine Learning Engineer, Co-Founder and CEO @overfit.studyTable of ContentsForeword xvPreface xviiAcknowledgments xxiAbout the Author xxiii Part I: Introduction to Large Language Models 1 Chapter 1: Overview of Large Language Models 3What Are Large Language Models? 4Popular Modern LLMs 20Domain-Specific LLMs 22Applications of LLMs 23Summary 29 Chapter 2: Semantic Search with LLMs 31Introduction 31The Task 32Solution Overview 34The Components 35Putting It All Together 51The Cost of Closed-Source Components 54Summary 55 Chapter 3: First Steps with Prompt Engineering 57Introduction 57Prompt Engineering 57Working with Prompts Across Models 65Building a Q/A Bot with ChatGPT 69Summary 74 Part II: Getting the Most Out of LLMs 75 Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77Introduction 77Transfer Learning and Fine-Tuning: A Primer 78A Look at the OpenAI Fine-Tuning API 82Preparing Custom Examples with the OpenAI CLI 84Setting Up the OpenAI CLI 87Our First Fine-Tuned LLM 88Case Study: Amazon Review Category Classification 93Summary 95 Chapter 5: Advanced Prompt Engineering 97Introduction 97Prompt Injection Attacks 97Input/Output Validation 99Batch Prompting 103Prompt Chaining 104Chain-of-Thought Prompting 111Revisiting Few-Shot Learning 113Testing and Iterative Prompt Development 123Summary 124 Chapter 6: Customizing Embeddings and Model Architectures 125Introduction 125Case Study: Building a Recommendation System 126Summary 144 Part III: Advanced LLM Usage 145 Chapter 7: Moving Beyond Foundation Models 147Introduction 147Case Study: Visual Q/A 147Case Study: Reinforcement Learning from Feedback 163Summary 173 Chapter 8: Advanced Open-Source LLM Fine-Tuning 175Introduction 175Example: Anime Genre Multilabel Classification with BERT 176Example: LaTeX Generation with GPT2 189Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193The Ever-Changing World of Fine-Tuning 206Summary 207 Chapter 9: Moving LLMs into Production 209Introduction 209Deploying Closed-Source LLMs to Production 209Deploying Open-Source LLMs to Production 210Summary 225 Part IV: Appendices 227 Appendix A: LLM FAQs 229Appendix B: LLM Glossary 233Appendix C: LLM Application Archetypes 239 Index 243
£34.19
O'Reilly Media AI at the Edge
Book SynopsisThis 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.
£47.99
Springer-Verlag New York Inc. Introduction to Biometrics
Book SynopsisIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.Table of ContentsIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.
£59.99
Cambridge University Press Machine Learning in the Real World
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£42.74
Cambridge University Press HandsOn Network Machine Learning with Python
£47.49
Cambridge University Press Machine Learning Refined
Book SynopsisWith 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 gradTrade Review'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'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'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'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'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'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'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, zbMATHTable of Contents1. 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.
£55.09
Cambridge University Press Foundations of Data Science
Book SynopsisThis 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 noTrade Review'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'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'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'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'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, ChoiceTable of Contents1. 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.
£42.74
Pearson Education (US) Design Patterns
Book SynopsisDr. Erich Gamma is 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. John Vlissides 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 Design Patterns: Elements of Reusable Object-Oriented Software, he is co-editor of the book Pattern Languages of Program Design 2 (both from Addison-Wesley). He and the other co-authors of Design Patterns are recipients of the 1998 Dr. Dobb's Journal Excellence in Programming Award. 020163Table of Contents 1. Introduction. 2. A Case Study: Designing a Document Editor. 3. Creational Patterns. 4. Structural Pattern. 5. Behavioral Patterns. 6. Conclusion. Appendix A: Glossary. Appendix B: Guide to Notation. Appendix C: Foundation Classes. Bibliography. Index.
£44.09
Pearson Education AI Prompt Engineering Absolute Beginners Guide
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£27.20
Elsevier Science Machine Learning for Biomedical Applications
Book SynopsisTable of Contents1. Programming in Python 2. Machine Learning Basics 3. Regression 4. Classification 5. Dimensionality reduction 6. Clustering 7. Ensemble methods 8. Feature extraction and selection 9. Introduction to Deep Learning 10. Neural Networks 11. Convolutional Neural Networks
£55.05
Cambridge University Press Mining of Massive Datasets
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
£61.74
Cambridge University Press Algorithmic HighDimensional Robust Statistics
Book SynopsisThis reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptions, including robustness to model misspecification and to adversarial outliers in the dataset.Trade Review'This is a timely book on efficient algorithms for computing robust statistics from noisy data. It presents lucid intuitive descriptions of the algorithms as well as precise statements of results with rigorous proofs - a nice combination indeed. The topic has seen fundamental breakthroughs over the last few years and the authors are among the leading contributors. The reader will get a ringside view of the developments.' Ravi Kannan, Visiting Professor, Indian Institute of ScienceTable of Contents1. Introduction to robust statistics; 2. Efficient high-dimensional robust mean estimation; 3. Algorithmic refinements in robust mean estimation; 4. Robust covariance estimation; 5. List-decodable learning; 6. Robust estimation via higher moments; 7. Robust supervised learning; 8. Information-computation tradeoffs in high-dimensional robust statistics; A. Mathematical background; References; Index.
£42.74
Springer Nature Switzerland AG Recent Advances in Ensembles for Feature Selection
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£80.99
Springer Nature Switzerland AG Intelligent Wavelet Based Techniques for Advanced Multimedia Applications
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£80.99
Springer Nature Switzerland AG Fundamentals of Music Processing: Using Python
Book SynopsisThe 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.The 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.As 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.Table of Contents1. 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.
£61.74
Springer International Publishing AG Pattern Recognition: 14th Mexican Conference, MCPR 2022, Ciudad Juárez, Mexico, June 22–25, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 14th Mexican Conference on Pattern Recognition, MCPR 2022, which was held in planned to be held Ciudad Juárez, Mexico, in June 2022. The 33 papers presented in this volume were carefully reviewed and selected from 66 submissions. They are organized in the following topical sections: pattern recognition techniques; neural networks and deep learning; image and signal processing and analysis; natural language processing and recognition; robotics and remote sensing applications of pattern recognition; medical applications of pattern recognition.Table of ContentsPattern Recognition Techniques.- Hot Spots & Hot Regions Detection using Classification Algorithms in BMPs Complexes at the Protein-protein Interface with the Ground-state Energy Feature.- Clustering of Twitter Networks based on Users’ Structural Profile.- Changing Model from NGSIM Dataset.- A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants.- A Preliminary Study of SMOTE on Imbalanced Big Datasets when Dealing with Sparse and Dense High Dimensionality.- A Novel Survival Analysis-based Approach for Predicting Behavioral Probability of Mining Mixed Data Bases using Machine Learning Algorithms.- Networks and Deep Learning A CNN-based Driver’s Drowsiness and Distraction Detection System.- 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain.- Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification.- Deep Variational Method with Attention for High-Definition Face Generation.- Indoor Air Pollution Forecasting using Deep Neural Networks.- Extreme Machine Learning Architectures based on Correlation.- Image & Signal Processing and Analysis Evaluating New Set of Acoustical Features for Cry Signal Classification.- Motor Imagery Classification Using Riemannian Geometry in Multiple Frequency Bands with a Weighted Nearest Neighbors Approach.- Virtualizing 3D Real Environments Using 2D Pictures Based on Photogrammetry.- Factorized U-net for Retinal Vessel Segmentation.- Multi-view Learning for EEG Signal Classification of Imagined Speech.- Escalante Emotion Recognition using Time-frequency Distribution and GLCM Features from EEG Signals.- Natural Language Processing and Recognition Leveraging Multiple Characterizations of Social Media Users for Depression Detection Using Data Fusion.- A Wide & Deep Learning Approach for Covid-19 Tweet Classification.- Does this Tweet Report an Adverse Drug Reaction? An Enhanced BERT-based Method to Identify Drugs Side Effects in Twitter.- We Will Know Them by Their Style: Fake News Detection based on Masked n-grams.- Multi-Document Text Summarization based on Genetic Algorithm and the Relevance of Sentence Features.- ´ Robotics & Remote Sensing Applications of Pattern Recognition On Labelling Pointclouds with the Nearest Facet of Triangulated Building Models.- Dust Deposition Classification on the Receiver Tube of the Parabolic Trough Collector: A Deep Learning-based Approach.- Detection of Pain Caused By A Thermal Stimulus Using EEG and Machine Learning.- Data Mining.- Natural Language Processing and Recognition.- Document Processing and Recognition.- Fuzzy and Hybrid Techniques in Pattern Recognition.- Image Coding, Processing and Analysis.- Industrial and Medical Applications of Pattern Recognition.- Bioinformatics.- Logical Combinatorial Pattern Recognition.- Mathematical Morphology.- Artificial Intelligence Techniques and Recognition.- Pattern Recognition Principles.- Robotics & Remote Sensing Applications of Pattern Recognition.- Shape and Texture Analysis.- Signal Processing and Analysis.
£58.49
De Gruyter Computational Intelligence in Software Modeling
Book SynopsisResearchers, 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.
£101.25
Springer International Publishing AG The Data Science Design Manual
Book SynopsisThis 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. The Data Science Design Manual 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. 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. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) Trade Review “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) Table of ContentsWhat is Data Science? Mathematical Preliminaries Data Munging Scores and Rankings Statistical Analysis Visualizing Data Mathematical Models Linear Algebra Linear and Logistic Regression Distance and Network Methods Machine Learning Big Data: Achieving Scale
£45.55
Springer International Publishing AG Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I
Book SynopsisThe two-volume set LNCS 10269 and 10270 constitutes the refereed proceedings of the 20th Scandinavian Conference on Image Analysis, SCIA 2017, held in Tromsø, Norway, in June 2017. The 87 revised papers presented were carefully reviewed and selected from 133 submissions. The contributions are structured in topical sections on history of SCIA; motion analysis and 3D vision; pattern detection and recognition; machine learning; image processing and applications; feature extraction and segmentation; remote sensing; medical and biomedical image analysis; faces, gestures and multispectral analysis.Table of ContentsHistory of SCIA.- Motion analysis and 3D vision.- Pattern detection and recognition.- Machine learning.- Image processing and applications.- Feature extraction and segmentation.- Remote sensing.- Medical and biomedical image analysis.- Faces, gestures and multispectral analysis.
£62.99
Springer Pattern Recognition and Machine Learning
Book SynopsisProbability 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.Trade ReviewFrom the reviews: "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." John Maindonald for the Journal of Statistical Software "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) "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) "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) "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) "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) "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) "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) "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)Table of ContentsProbability 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.
£67.49
John Wiley & Sons Inc Statistical Pattern Recognition
Book SynopsisStatistical 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.Trade Review"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.” (Zentralblatt MATH, 1 December 2012)Table of ContentsPreface xix Notation xxiii 1 Introduction to Statistical Pattern Recognition 1 1.1 Statistical Pattern Recognition 1 1.1.1 Introduction 1 1.1.2 The Basic Model 2 1.2 Stages in a Pattern Recognition Problem 4 1.3 Issues 6 1.4 Approaches to Statistical Pattern Recognition 7 1.5 Elementary Decision Theory 8 1.5.1 Bayes’ Decision Rule for Minimum Error 8 1.5.2 Bayes’ Decision Rule for Minimum Error – Reject Option 12 1.5.3 Bayes’ Decision Rule for Minimum Risk 13 1.5.4 Bayes’ Decision Rule for Minimum Risk – Reject Option 15 1.5.5 Neyman–Pearson Decision Rule 15 1.5.6 Minimax Criterion 18 1.5.7 Discussion 19 1.6 Discriminant Functions 20 1.6.1 Introduction 20 1.6.2 Linear Discriminant Functions 21 1.6.3 Piecewise Linear Discriminant Functions 23 1.6.4 Generalised Linear Discriminant Function 24 1.6.5 Summary 26 1.7 Multiple Regression 27 1.8 Outline of Book 29 1.9 Notes and References 29 Exercises 31 2 Density Estimation – Parametric 33 2.1 Introduction 33 2.2 Estimating the Parameters of the Distributions 34 2.2.1 Estimative Approach 34 2.2.2 Predictive Approach 35 2.3 The Gaussian Classifier 35 2.3.1 Specification 35 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 37 2.3.3 Example Application Study 39 2.4 Dealing with Singularities in the Gaussian Classifier 40 2.4.1 Introduction 40 2.4.2 Na¨ive Bayes 40 2.4.3 Projection onto a Subspace 41 2.4.4 Linear Discriminant Function 41 2.4.5 Regularised Discriminant Analysis 42 2.4.6 Example Application Study 44 2.4.7 Further Developments 45 2.4.8 Summary 46 2.5 Finite Mixture Models 46 2.5.1 Introduction 46 2.5.2 Mixture Models for Discrimination 48 2.5.3 Parameter Estimation for Normal Mixture Models 49 2.5.4 Normal Mixture Model Covariance Matrix Constraints 51 2.5.5 How Many Components? 52 2.5.6 Maximum Likelihood Estimation via EM 55 2.5.7 Example Application Study 60 2.5.8 Further Developments 62 2.5.9 Summary 63 2.6 Application Studies 63 2.7 Summary and Discussion 66 2.8 Recommendations 66 2.9 Notes and References 67 Exercises 67 3 Density Estimation – Bayesian 70 3.1 Introduction 70 3.1.1 Basics 72 3.1.2 Recursive Calculation 72 3.1.3 Proportionality 73 3.2 Analytic Solutions 73 3.2.1 Conjugate Priors 73 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance 75 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution 79 3.2.4 Unknown Prior Class Probabilities 85 3.2.5 Summary 87 3.3 Bayesian Sampling Schemes 87 3.3.1 Introduction 87 3.3.2 Summarisation 87 3.3.3 Sampling Version of the Bayesian Classifier 89 3.3.4 Rejection Sampling 89 3.3.5 Ratio of Uniforms 90 3.3.6 Importance Sampling 92 3.4 Markov Chain Monte Carlo Methods 95 3.4.1 Introduction 95 3.4.2 The Gibbs Sampler 95 3.4.3 Metropolis–Hastings Algorithm 103 3.4.4 Data Augmentation 107 3.4.5 Reversible Jump Markov Chain Monte Carlo 108 3.4.6 Slice Sampling 109 3.4.7 MCMC Example – Estimation of Noisy Sinusoids 111 3.4.8 Summary 115 3.4.9 Notes and References 116 3.5 Bayesian Approaches to Discrimination 116 3.5.1 Labelled Training Data 116 3.5.2 Unlabelled Training Data 117 3.6 Sequential Monte Carlo Samplers 119 3.6.1 Introduction 119 3.6.2 Basic Methodology 121 3.6.3 Summary 125 3.7 Variational Bayes 126 3.7.1 Introduction 126 3.7.2 Description 126 3.7.3 Factorised Variational Approximation 129 3.7.4 Simple Example 131 3.7.5 Use of the Procedure for Model Selection 135 3.7.6 Further Developments and Applications 136 3.7.7 Summary 137 3.8 Approximate Bayesian Computation 137 3.8.1 Introduction 137 3.8.2 ABC Rejection Sampling 138 3.8.3 ABC MCMC Sampling 140 3.8.4 ABC Population Monte Carlo Sampling 141 3.8.5 Model Selection 142 3.8.6 Summary 143 3.9 Example Application Study 144 3.10 Application Studies 145 3.11 Summary and Discussion 146 3.12 Recommendations 147 3.13 Notes and References 147 Exercises 148 4 Density Estimation – Nonparametric 150 4.1 Introduction 150 4.1.1 Basic Properties of Density Estimators 150 4.2 k-Nearest-Neighbour Method 152 4.2.1 k-Nearest-Neighbour Classifier 152 4.2.2 Derivation 154 4.2.3 Choice of Distance Metric 157 4.2.4 Properties of the Nearest-Neighbour Rule 159 4.2.5 Linear Approximating and Eliminating Search Algorithm 159 4.2.6 Branch and Bound Search Algorithms: kd-Trees 163 4.2.7 Branch and Bound Search Algorithms: Ball-Trees 170 4.2.8 Editing Techniques 174 4.2.9 Example Application Study 177 4.2.10 Further Developments 178 4.2.11 Summary 179 4.3 Histogram Method 180 4.3.1 Data Adaptive Histograms 181 4.3.2 Independence Assumption (Na¨ive Bayes) 181 4.3.3 Lancaster Models 182 4.3.4 Maximum Weight Dependence Trees 183 4.3.5 Bayesian Networks 186 4.3.6 Example Application Study – Na¨ive Bayes Text Classification 190 4.3.7 Summary 193 4.4 Kernel Methods 194 4.4.1 Biasedness 197 4.4.2 Multivariate Extension 198 4.4.3 Choice of Smoothing Parameter 199 4.4.4 Choice of Kernel 201 4.4.5 Example Application Study 202 4.4.6 Further Developments 203 4.4.7 Summary 203 4.5 Expansion by Basis Functions 204 4.6 Copulas 207 4.6.1 Introduction 207 4.6.2 Mathematical Basis 207 4.6.3 Copula Functions 208 4.6.4 Estimating Copula Probability Density Functions 209 4.6.5 Simple Example 211 4.6.6 Summary 212 4.7 Application Studies 213 4.7.1 Comparative Studies 216 4.8 Summary and Discussion 216 4.9 Recommendations 217 4.10 Notes and References 217 Exercises 218 5 Linear Discriminant Analysis 221 5.1 Introduction 221 5.2 Two-Class Algorithms 222 5.2.1 General Ideas 222 5.2.2 Perceptron Criterion 223 5.2.3 Fisher’s Criterion 227 5.2.4 Least Mean-Squared-Error Procedures 228 5.2.5 Further Developments 235 5.2.6 Summary 235 5.3 Multiclass Algorithms 236 5.3.1 General Ideas 236 5.3.2 Error-Correction Procedure 237 5.3.3 Fisher’s Criterion – Linear Discriminant Analysis 238 5.3.4 Least Mean-Squared-Error Procedures 241 5.3.5 Regularisation 246 5.3.6 Example Application Study 246 5.3.7 Further Developments 247 5.3.8 Summary 248 5.4 Support Vector Machines 249 5.4.1 Introduction 249 5.4.2 Linearly Separable Two-Class Data 249 5.4.3 Linearly Nonseparable Two-Class Data 253 5.4.4 Multiclass SVMs 256 5.4.5 SVMs for Regression 257 5.4.6 Implementation 259 5.4.7 Example Application Study 262 5.4.8 Summary 263 5.5 Logistic Discrimination 263 5.5.1 Two-Class Case 263 5.5.2 Maximum Likelihood Estimation 264 5.5.3 Multiclass Logistic Discrimination 266 5.5.4 Example Application Study 267 5.5.5 Further Developments 267 5.5.6 Summary 268 5.6 Application Studies 268 5.7 Summary and Discussion 268 5.8 Recommendations 269 5.9 Notes and References 270 Exercises 270 6 Nonlinear Discriminant Analysis – Kernel and Projection Methods 274 6.1 Introduction 274 6.2 Radial Basis Functions 276 6.2.1 Introduction 276 6.2.2 Specifying the Model 278 6.2.3 Specifying the Functional Form 278 6.2.4 The Positions of the Centres 279 6.2.5 Smoothing Parameters 281 6.2.6 Calculation of the Weights 282 6.2.7 Model Order Selection 284 6.2.8 Simple RBF 285 6.2.9 Motivation 286 6.2.10 RBF Properties 288 6.2.11 Example Application Study 288 6.2.12 Further Developments 289 6.2.13 Summary 290 6.3 Nonlinear Support Vector Machines 291 6.3.1 Introduction 291 6.3.2 Binary Classification 291 6.3.3 Types of Kernel 292 6.3.4 Model Selection 293 6.3.5 Multiclass SVMs 294 6.3.6 Probability Estimates 294 6.3.7 Nonlinear Regression 296 6.3.8 Example Application Study 296 6.3.9 Further Developments 297 6.3.10 Summary 298 6.4 The Multilayer Perceptron 298 6.4.1 Introduction 298 6.4.2 Specifying the MLP Structure 299 6.4.3 Determining the MLP Weights 300 6.4.4 Modelling Capacity of the MLP 307 6.4.5 Logistic Classification 307 6.4.6 Example Application Study 310 6.4.7 Bayesian MLP Networks 311 6.4.8 Projection Pursuit 313 6.4.9 Summary 313 6.5 Application Studies 314 6.6 Summary and Discussion 316 6.7 Recommendations 317 6.8 Notes and References 318 Exercises 318 7 Rule and Decision Tree Induction 322 7.1 Introduction 322 7.2 Decision Trees 323 7.2.1 Introduction 323 7.2.2 Decision Tree Construction 326 7.2.3 Selection of the Splitting Rule 327 7.2.4 Terminating the Splitting Procedure 330 7.2.5 Assigning Class Labels to Terminal Nodes 332 7.2.6 Decision Tree Pruning – Worked Example 332 7.2.7 Decision Tree Construction Methods 337 7.2.8 Other Issues 339 7.2.9 Example Application Study 340 7.2.10 Further Developments 341 7.2.11 Summary 342 7.3 Rule Induction 342 7.3.1 Introduction 342 7.3.2 Generating Rules from a Decision Tree 345 7.3.3 Rule Induction Using a Sequential Covering Algorithm 345 7.3.4 Example Application Study 350 7.3.5 Further Developments 351 7.3.6 Summary 351 7.4 Multivariate Adaptive Regression Splines 351 7.4.1 Introduction 351 7.4.2 Recursive Partitioning Model 351 7.4.3 Example Application Study 355 7.4.4 Further Developments 355 7.4.5 Summary 356 7.5 Application Studies 356 7.6 Summary and Discussion 358 7.7 Recommendations 358 7.8 Notes and References 359 Exercises 359 8 Ensemble Methods 361 8.1 Introduction 361 8.2 Characterising a Classifier Combination Scheme 362 8.2.1 Feature Space 363 8.2.2 Level 366 8.2.3 Degree of Training 368 8.2.4 Form of Component Classifiers 368 8.2.5 Structure 369 8.2.6 Optimisation 369 8.3 Data Fusion 370 8.3.1 Architectures 370 8.3.2 Bayesian Approaches 371 8.3.3 Neyman–Pearson Formulation 373 8.3.4 Trainable Rules 374 8.3.5 Fixed Rules 375 8.4 Classifier Combination Methods 376 8.4.1 Product Rule 376 8.4.2 Sum Rule 377 8.4.3 Min, Max and Median Combiners 378 8.4.4 Majority Vote 379 8.4.5 Borda Count 379 8.4.6 Combiners Trained on Class Predictions 380 8.4.7 Stacked Generalisation 382 8.4.8 Mixture of Experts 382 8.4.9 Bagging 385 8.4.10 Boosting 387 8.4.11 Random Forests 389 8.4.12 Model Averaging 390 8.4.13 Summary of Methods 396 8.4.14 Example Application Study 398 8.4.15 Further Developments 399 8.5 Application Studies 399 8.6 Summary and Discussion 400 8.7 Recommendations 401 8.8 Notes and References 401 Exercises 402 9 Performance Assessment 404 9.1 Introduction 404 9.2 Performance Assessment 405 9.2.1 Performance Measures 405 9.2.2 Discriminability 406 9.2.3 Reliability 413 9.2.4 ROC Curves for Performance Assessment 415 9.2.5 Population and Sensor Drift 419 9.2.6 Example Application Study 421 9.2.7 Further Developments 422 9.2.8 Summary 423 9.3 Comparing Classifier Performance 424 9.3.1 Which Technique is Best? 424 9.3.2 Statistical Tests 425 9.3.3 Comparing Rules When Misclassification Costs are Uncertain 426 9.3.4 Example Application Study 428 9.3.5 Further Developments 429 9.3.6 Summary 429 9.4 Application Studies 429 9.5 Summary and Discussion 430 9.6 Recommendations 430 9.7 Notes and References 430 Exercises 431 10 Feature Selection and Extraction 433 10.1 Introduction 433 10.2 Feature Selection 435 10.2.1 Introduction 435 10.2.2 Characterisation of Feature Selection Approaches 439 10.2.3 Evaluation Measures 440 10.2.4 Search Algorithms for Feature Subset Selection 449 10.2.5 Complete Search – Branch and Bound 450 10.2.6 Sequential Search 454 10.2.7 Random Search 458 10.2.8 Markov Blanket 459 10.2.9 Stability of Feature Selection 460 10.2.10 Example Application Study 462 10.2.11 Further Developments 462 10.2.12 Summary 463 10.3 Linear Feature Extraction 463 10.3.1 Principal Components Analysis 464 10.3.2 Karhunen–Lo`eve Transformation 475 10.3.3 Example Application Study 481 10.3.4 Further Developments 482 10.3.5 Summary 483 10.4 Multidimensional Scaling 484 10.4.1 Classical Scaling 484 10.4.2 Metric MDS 486 10.4.3 Ordinal Scaling 487 10.4.4 Algorithms 490 10.4.5 MDS for Feature Extraction 491 10.4.6 Example Application Study 492 10.4.7 Further Developments 493 10.4.8 Summary 493 10.5 Application Studies 493 10.6 Summary and Discussion 495 10.7 Recommendations 495 10.8 Notes and References 496 Exercises 497 11 Clustering 501 11.1 Introduction 501 11.2 Hierarchical Methods 502 11.2.1 Single-Link Method 503 11.2.2 Complete-Link Method 506 11.2.3 Sum-of-Squares Method 507 11.2.4 General Agglomerative Algorithm 508 11.2.5 Properties of a Hierarchical Classification 508 11.2.6 Example Application Study 509 11.2.7 Summary 509 11.3 Quick Partitions 510 11.4 Mixture Models 511 11.4.1 Model Description 511 11.4.2 Example Application Study 512 11.5 Sum-of-Squares Methods 513 11.5.1 Clustering Criteria 514 11.5.2 Clustering Algorithms 515 11.5.3 Vector Quantisation 520 11.5.4 Example Application Study 530 11.5.5 Further Developments 530 11.5.6 Summary 531 11.6 Spectral Clustering 531 11.6.1 Elementary Graph Theory 531 11.6.2 Similarity Matrices 534 11.6.3 Application to Clustering 534 11.6.4 Spectral Clustering Algorithm 535 11.6.5 Forms of Graph Laplacian 535 11.6.6 Example Application Study 536 11.6.7 Further Developments 538 11.6.8 Summary 538 11.7 Cluster Validity 538 11.7.1 Introduction 538 11.7.2 Statistical Tests 539 11.7.3 Absence of Class Structure 540 11.7.4 Validity of Individual Clusters 541 11.7.5 Hierarchical Clustering 542 11.7.6 Validation of Individual Clusterings 542 11.7.7 Partitions 543 11.7.8 Relative Criteria 543 11.7.9 Choosing the Number of Clusters 545 11.8 Application Studies 546 11.9 Summary and Discussion 549 11.10 Recommendations 551 11.11 Notes and References 552 Exercises 553 12 Complex Networks 555 12.1 Introduction 555 12.1.1 Characteristics 557 12.1.2 Properties 557 12.1.3 Questions to Address 559 12.1.4 Descriptive Features 560 12.1.5 Outline 560 12.2 Mathematics of Networks 561 12.2.1 Graph Matrices 561 12.2.2 Connectivity 562 12.2.3 Distance Measures 562 12.2.4 Weighted Networks 563 12.2.5 Centrality Measures 563 12.2.6 Random Graphs 564 12.3 Community Detection 565 12.3.1 Clustering Methods 565 12.3.2 Girvan–Newman Algorithm 568 12.3.3 Modularity Approaches 570 12.3.4 Local Modularity 571 12.3.5 Clique Percolation 573 12.3.6 Example Application Study 574 12.3.7 Further Developments 575 12.3.8 Summary 575 12.4 Link Prediction 575 12.4.1 Approaches to Link Prediction 576 12.4.2 Example Application Study 578 12.4.3 Further Developments 578 12.5 Application Studies 579 12.6 Summary and Discussion 579 12.7 Recommendations 580 12.8 Notes and References 580 Exercises 580 13 Additional Topics 581 13.1 Model Selection 581 13.1.1 Separate Training and Test Sets 582 13.1.2 Cross-Validation 582 13.1.3 The Bayesian Viewpoint 583 13.1.4 Akaike’s Information Criterion 583 13.1.5 Minimum Description Length 584 13.2 Missing Data 585 13.3 Outlier Detection and Robust Procedures 586 13.4 Mixed Continuous and Discrete Variables 587 13.5 Structural Risk Minimisation and the Vapnik–Chervonenkis Dimension 588 13.5.1 Bounds on the Expected Risk 588 13.5.2 The VC Dimension 589 References 591 Index 637
£51.25
John Wiley & Sons Inc Pattern Classification
Book SynopsisPATTERN CLASSIFICATION a unified view of statistical and neural approaches 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. Offering a lucid presentation of complex appTable of ContentsStatistical Decision Theory. Need for Approximations: Fundamental Approaches. Classification Based on Statistical Models Determined by First-and-Second Order Statistical Moments. Classification Based on Mean-Square Functional Approximations. Polynomial Regression. Multilayer Perceptron Regression. Radial Basis Functions. Measurements, Features, and Feature Section. Reject Criteria and Classifier Performance. Combining Classifiers. Conclusion. STATMOD Program: Description of ftp Package. References. Index.
£150.26
John Wiley & Sons Inc Geometric Data Analysis An Empirical Approach to
Book SynopsisThis 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.Trade Review"...provides a valuable summary of data reduction." (Technometrics, May 2002) "...effectively describes and summarizes an emerging new field, namely, scientific data modeling and analysis." (Mathematical Reviews, 2003h)Table of ContentsPreface. Acknowledgments. INTRODUCTION. Pattern Analysis as Data Reduction. Vector Spaces and Linear Transformations. OPTIMAL ORTHOGONAL PATTERN REPRESENTATIONS. The Karhunen-Loève Expansion. Additional Theory, Algorithms and Applications. TIME, FREQUENCY AND SCALE ANALYSIS. Fourier Analysis. Wavelet Expansions. ADAPTIVE NONLINEAR MAPPINGS. Radial Basis Functions. Neural Networks. Nonlinear Reduction Architectures. Appendix A Mathemetical Preliminaries. References. Index.
£107.06
John Wiley & Sons Inc Speech Coding Algorithms Foundation and Evolution
Book SynopsisSpeech 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).Trade Review“…well equipped with exercises and with procedures which are helpful in implementing the coders…” (Zentralblatt Math, Vol.1041, No.16, 2004)Table of ContentsPreface xiii Acronyms xix Notation xxiii 1 Introduction 1 1.1 Overview of Speech Coding 2 1.2 Classification of Speech Coders 8 1.3 Speech Production and Modeling 11 1.4 Some Properties of the Human Auditory System 18 1.5 Speech Coding Standards 22 1.6 About Algorithms 26 1.7 Summary and References 31 2 Signal Processing Techniques 33 2.1 Pitch Period Estimation 33 2.2 All-Pole and All-Zero Filters 45 2.3 Convolution 52 2.4 Summary and References 57 Exercises 57 3 Stochastic Processes and Models 61 3.1 Power Spectral Density 62 3.2 Periodogram 67 3.3 Autoregressive Model 69 3.4 Autocorrelation Estimation 73 3.5 Other Signal Models 85 3.6 Summary and References 86 Exercises 87 4 Linear Prediction 91 4.1 The Problem of Linear Prediction 92 4.2 Linear Prediction Analysis of Nonstationary Signals 96 4.3 Examples of Linear Prediction Analysis of Speech 101 4.4 The Levinson–Durbin Algorithm 107 4.5 The Leroux–Gueguen Algorithm 114 4.6 Long-Term Linear Prediction 120 4.7 Synthesis Filters 127 4.8 Practical Implementation 131 4.9 Moving Average Prediction 137 4.10 Summary and References 138 Exercises 139 5 Scalar Quantization 143 5.1 Introduction 143 5.2 Uniform Quantizer 147 5.3 Optimal Quantizer 149 5.4 Quantizer Design Algorithms 151 5.5 Algorithmic Implementation 155 5.6 Summary and References 158 Exercises 158 6 Pulse Code Modulation and Its Variants 161 6.1 Uniform Quantization 161 6.2 Nonuniform Quantization 166 6.3 Differential Pulse Code Modulation 172 6.4 Adaptive Schemes 175 6.5 Summary and References 180 Exercises 181 7 Vector Quantization 184 7.1 Introduction 185 7.2 Optimal Quantizer 188 7.3 Quantizer Design Algorithms 189 7.4 Multistage VQ 194 7.5 Predictive VQ 216 7.6 Other Structured Schemes 219 7.7 Summary and References 221 Exercises 222 8 Scalar Quantization of Linear Prediction Coefficient 227 8.1 Spectral Distortion 227 8.2 Quantization Based on Reflection Coefficient and Log Area Ratio 232 8.3 Line Spectral Frequency 239 8.4 Quantization Based on Line Spectral Frequency 252 8.5 Interpolation of LPC 256 8.6 Summary and References 258 Exercises 260 9 Linear Prediction Coding 263 9.1 Speech Production Model 264 9.2 Structure of the Algorithm 268 9.3 Voicing Detector 271 9.4 The FS1015 LPC Coder 275 9.5 Limitations of the LPC Model 277 9.6 Summary and References 280 Exercises 281 10 Regular-pulse Excitation Coders 285 10.1 Multipulse Excitation Model 286 10.2 Regular-Pulse-Excited–Long-Term Prediction 289 10.3 Summary and References 295 Exercises 296 11 Code-excited Linear Prediction 299 11.1 The CELP Speech Production Model 300 11.2 The Principle of Analysis-by-Synthesis 301 11.3 Encoding and Decoding 302 11.4 Excitation Codebook Search 308 11.5 Postfilter 317 11.6 Summary and References 325 Exercises 326 12 The Federal Standard Version of CELP 330 12.1 Improving the Long-Term Predictor 331 12.2 The Concept of the Adaptive Codebook 333 12.3 Incorporation of the Adaptive Codebook to the CELP Framework 336 12.4 Stochastic Codebook Structure 338 12.5 Adaptive Codebook Search 341 12.6 Stochastic Codebook Search 344 12.7 Encoder and Decoder 346 12.8 Summary and References 349 Exercises 350 13 Vector Sum Excited Linear Prediction 353 13.1 The Core Encoding Structure 354 13.2 Search Strategies for Excitation Codebooks 356 13.3 Excitation Codebook Searches 357 13.4 Gain Related Procedures 362 13.5 Encoder and Decoder 366 13.6 Summary and References 368 Exercises 369 14 Low-delay CELP 372 14.1 Strategies to Achieve Low Delay 373 14.2 Basic Operational Principles 375 14.3 Linear Prediction Analysis 377 14.4 Excitation Codebook Search 380 14.5 Backward Gain Adaptation 385 14.6 Encoder and Decoder 389 14.7 Codebook Training 391 14.8 Summary and References 393 Exercises 394 15 Vector Quantization of Linear Prediction Coefficient 396 15.1 Correlation Among the LSFs 396 15.2 Split VQ 399 15.3 Multistage VQ 403 15.4 Predictive VQ 407 15.5 Summary and References 418 Exercises 419 16 Algebraic CELP 423 16.1 Algebraic Codebook Structure 424 16.2 Adaptive Codebook 425 16.3 Encoding and Decoding 433 16.4 Algebraic Codebook Search 437 16.5 Gain Quantization Using Conjugate VQ 443 16.6 Other ACELP Standards 446 16.7 Summary and References 451 Exercises 451 17 Mixed Excitation Linear Prediction 454 17.1 The MELP Speech Production Model 455 17.2 Fourier Magnitudes 456 17.3 Shaping Filters 464 17.4 Pitch Period and Voicing Strength Estimation 466 17.5 Encoder Operations 474 17.6 Decoder Operations 477 17.7 Summary and References 481 Exercises 482 18 Source-controlled Variable Bit-rate CELP 486 18.1 Adaptive Rate Decision 487 18.2 LP Analysis and LSF-Related Operations 494 18.3 Decoding and Encoding 496 18.4 Summary and References 498 Exercises 499 19 Speech Quality Assessment 501 19.1 The Scope of Quality and Measuring Conditions 501 19.2 Objective Quality Measurements for Waveform Coders 502 19.3 Subjective Quality Measures 504 19.4 Improvements on Objective Quality Measures 505 Appendix A Minimum-phase Property of the Forward Prediction-error Filter 507 Appendix B Some Properties of Line Spectral Frequency 514 Appendix C Research Directions in Speech Coding 518 Appendix D Linear Combiner for Pattern Classification 522 Appendix E CELP: Optimal Long-term Predictor to Minimize the Weighted Difference 531 Appendix F Review of Linear Algebra: Orthogonality, Basis, Linear Independence, and the Gram–schmidt Algorithm 537 Bibliography 542 Index 553
£164.66
IEEE Computer Society Press,U.S. The Pattern Recognition Basis of Artificial
Book Synopsis
£95.36
ISTE Ltd Change Detection and Image Time Series Analysis
Book SynopsisChange Detection and Image Time Series Analysis 2 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.Chapter 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.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 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,Chapters 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.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253
£124.15
ISTE Ltd and John Wiley & Sons Inc Eyestrain Reduction in Stereoscopy
Book SynopsisStereoscopic 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. This 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.Table of ContentsAcknowledgments ix Introduction xi Chapter 1. Principles of Depth and Shape Perception 1 1.1. Function of the eye 1 1.2. Depth perception without stereoscopy 2 1.2.1. Monocular cues 2 1.2.2. Proprioceptive cues 7 1.3. Depth perception through stereoscopic vision 9 1.4. Perception of inclinations and curves 10 1.4.1. Perception of inclination and obliqueness 10 1.4.2. Perception of curves 14 1.5. Artificial stereoscopic vision 22 Chapter 2. Technological Elements 25 2.1. Taking a picture 25 2.2. Reproduction 26 2.2.1. Colorimetric differentiation 27 2.2.2. Differentiation by polarization 28 2.2.3. Active glasses 30 2.2.4. Auto-stereoscopic screens 31 2.2.5. Virtual reality headsets 33 2.3. Motion parallax restitution 34 2.3.1. Pseudoscopic movement 34 2.3.2. Correcting pseudoscopic movements 35 2.3.3. Monoscopic motion parallax 40 Chapter 3. Causes of Visual Fatigue in Stereoscopic Vision 41 3.1. Conflict between accommodation and convergence 41 3.2. Too much depth 44 3.3. High spatial frequencies 46 3.3.1. Limits of fusion 49 3.3.2. Comfort and high frequencies. 50 3.4. High temporal frequency 52 3.5. Conflicts with monoscopic cues 52 3.6. Vertical disparities 53 3.7. Improper device settings 55 3.7.1. Quality of image and display 55 3.7.2. Differences between left and right images 56 3.7.3. Speed of correction of pseudoscopic movements 57 Chapter 4. Short- and Long-term Consequences 59 4.1. Short-term effects 59 4.1.1. Decreasing ease of accommodation 59 4.1.2. Decrease in stereoscopic acuity 59 4.1.3. Effects on the punctum proximum 61 4.1.4. More subjective effects 61 4.2. Long-term consequences 62 4.2.1. Long-term effects on children 62 Chapter 5. Measuring Visual Fatigue 63 5.1. Visual acuity 63 5.1.1. Different possible measurements 64 5.1.2. Optotypes 64 5.2. Proximum accommodation function 65 5.3. Ease of accommodation 66 5.4. Stereoscopic acuity 67 5.4.1. Tests of distance vision 67 5.4.2. Tests of near vision 68 5.5. Disassociated heterophorias 71 5.6. Fusional reserves 72 5.7. Subjective tests 74 Chapter 6. Reducing Spatial Frequencies 75 6.1. Principle 75 6.2. Technical solution 75 6.2.1. Wavelets 76 6.2.2. BOX FILTER 92 6.2.3. Using a rolling average and other “blurs” 98 6.2.4. Comparison of algorithms 103 6.2.5. Chosen solution 114 6.3. Experiment 116 6.3.1. The task 116 6.4. Measurements of fatigue taken 118 6.4.1. Objective measurements 118 6.4.2. Procedure 119 6.4.3. The subjects 120 6.5. Result 120 6.5.1. Proximum accommodation function 120 6.5.2. Ease of accommodation 121 6.5.3. Stereoscopic acuity 122 6.5.4. Effectiveness in execution of the task 122 6.5.5. Subjective measurements 123 6.5.6. Conclusions 124 6.5.7. Discussion 124 Chapter 7. Reducing the Distance Between the Virtual Cameras 131 7.1. Principle 131 7.1.1. Usefulness of stereoscopy in depth perception 132 7.1.2. The objects 133 7.1.3. Hypothesis 142 7.2. Experiment 142 7.2.1. Tasks 142 7.2.2. Experimental conditions 143 7.2.3. Subjects 144 7.2.4. Measurements 144 7.3. Results 145 7.3.1. Results for fatigue 145 7.3.2. Perception results 147 7.4. Discussion 152 7.4.1. Influence on visual fatigue 152 7.4.2. Influence on visual perception 153 Conclusion 155 Bibliography 157 Index 167
£125.06
De Gruyter Imaging Science
Book Synopsis
£129.67
Springer Signal Processing Methods for Music Transcription
Book SynopsisFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.Table of ContentsFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.
£123.49
Cambridge University Press Flexible Pattern Matching in Strings
Book SynopsisPresents recently developed algorithms for searching for simple, multiple and extended strings, regular expressions, exact and approximate matches.Trade Review'If you need efficient pattern matching for any kind of string then this is the only book I know that comes even close to providing you [with] the tools for the job.' The Journal of the ACCU'I really enjoyed reading and studying this book. I am convinced it is a must-read, especially chapters 4 through 6, for anyone who is involved in the task of designing algorithms for modern string or sequence matching.' Computing ReviewsTable of Contents1. Introduction; 2. String matching; 3. Multiple string matching; 4. Extended string matching; 5. Regular expression matching; 6. Approximate matching; 7. Conclusion; Bibliography; Index.
£53.99
Cambridge University Press Optical Pattern Recognition
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£60.10
Cambridge University Press Multibiometrics for Human Identification
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£137.09
Cambridge University Press Correlation Pattern Recognition
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Cambridge University Press Density Ratio Estimation in Machine Learning
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£124.00
Cambridge University Press Scaling Up Machine Learning
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Cambridge University Press Planning English Sentences
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£42.31
Cambridge University Press Correlation Pattern Recognition
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£133.00
Cambridge University Press An Introduction to Support Vector Machines and Other Kernelbased Learning Methods
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£79.99
Cambridge University Press Kernel Methods for Pattern Analysis
Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.Trade Review'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR NewsletterTable of ContentsPreface; Part I. Basic Concepts: 1. Pattern analysis; 2. Kernel methods: an overview; 3. Properties of kernels; 4. Detecting stable patterns; Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space; 6. Pattern analysis using eigen-decompositions; 7. Pattern analysis using convex optimisation; 8. Ranking, clustering and data visualisation; Part III. Constructing Kernels: 9. Basic kernels and kernel types; 10. Kernels for text; 11. Kernels for structured data: strings, trees, etc.; 12. Kernels from generative models; Appendix A: proofs omitted from the main text; Appendix B: notational conventions; Appendix C: list of pattern analysis methods; Appendix D: list of kernels; References; Index.
£86.99
Cambridge University Press Applied Combinatorics on Words 105 Encyclopedia of Mathematics and its Applications Series Number 105
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£159.00
Cambridge University Press Face Geometry and Appearance Modeling
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£66.99
Springer Seismic Modelling and Pattern Recognition in Oil Exploration
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£85.49
Cambridge University Press Understanding Machine Learning From Theory to
Book SynopsisMachine 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.Trade Review'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'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'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, BerkeleyTable of Contents1. 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.
£59.11
Cambridge University Press The Cambridge Handbook of Cognitive Linguistics
Book SynopsisThe best survey of cognitive linguistics available, this Handbook provides a thorough explanation of its rich methodology, key results, and interdisciplinary context. With in-depth coverage of the research questions, basic concepts, and various theoretical approaches, the Handbook addresses newly emerging subfields and shows their contribution to the discipline. The Handbook introduces fields of study that have become central to cognitive linguistics, such as conceptual mappings and construction grammar. It explains all the main areas of linguistic analysis traditionally expected in a full linguistics framework, and includes fields of study such as language acquisition, sociolinguistics, diachronic studies, and corpus linguistics. Setting linguistic facts within the context of many other disciplines, the Handbook will be welcomed by researchers and students in a broad range of disciplines, including linguistics, cognitive science, neuroscience, gesture studies, computational linguisticTrade ReviewAdvance praise: 'This is the definitive introduction to cognitive linguistics that the mature field deserves, written by the leading practitioners in cognitive approaches to grammar, semantics, conceptual structure, phonology, and everything in-between (and all around). I can't imagine a better introduction for students of language.' Benjamin K. Bergen, University of California, San DiegoTable of ContentsIntroduction Barbara Dancygier; Part I. Language in Cognition and Culture: 1. Opening commentary: language in cognition and culture N. J. Enfield; 2. Relationships between language and cognition Daniel Casasanto; 3. The study of indigenous languages Sally Rice; 4. First language acquisition Laura E. De Ruiter and Anna L. Theakston; 5. Second language acquisition Andrea Tyler; Part II. Language, Body, and Multimodal Communication: 6. Opening commentary: polytropos and communication in the wild Mark Turner; 7. Signed languages Sherman Wilcox and Corinne Occhino; 8. Gesture, language, and cognition Kensy Cooperrider and Susan Goldin-Meadow; 9. Multimodality in interaction Kurt Feyaerts, Geert Brône and Bert Oben; 10. Viewpoint Lieven Vandelanotte; 11. Embodied intersubjectivity Jordan Zlatev; 12. Intersubjectivity and grammar Ronny Boogaart and Alex Reuneker; Part III. Aspects of Linguistic Analysis: 13. Opening commentary: linguistic analysis John Newman; 14. Phonology Geoffrey S. Nathan; 15. The construction of words Geert Booij; 16. Lexical semantics John R. Taylor; 17. Cognitive grammar Ronald W. Langacker; 18. From constructions to construction grammars Thomas Hoffmann; 19. Construction grammars Thomas Hoffmann; 20. Cognitive linguistics and pragmatics Kerstin Fischer; 21. Fictive interaction Esther Pascual and Todd Oakley; 22. Diachronic approaches Alexander Bergs; Part IV. Conceptual Mappings: 23. Opening commentary: conceptual mappings Eve Sweetser; 24. Conceptual metaphor Karen Sullivan; 25. Metonymy Jeannette Littlemore; 26. Conceptual blending theory Todd Oakley and Esther Pascual; 27. Embodiment Raymond W. Gibbs, Jr; 28. Corpus linguistics and metaphor Elena Semino; 29. Metaphor, simulation, and fictive motion Teenie Matlock; Part V. Methodological Approaches: 30. Opening commentary: getting the measure of meaning Chris Sinha; 31. The quantitative turn Laura A. Janda; 32. Language and the brain Seana Coulson; 33. Cognitive sociolinguistics Willem B. Hollmann; 34. Computational resources: framenet and constructicon Hans C. Boas; 35. Computational approaches to metaphor: the case of MetaNet Oana A. David; 36. Corpus approaches Stefan Gries; 37. Cognitive linguistics and the study of textual meaning Barbara Dancygier; Part VI. Concepts and Approaches: Space and Time: 38. Linguistic patterns of space and time vocabulary Eve Sweetser and Alice Gaby; 39. Space-time mappings beyond language Alice Gaby and Eve Sweetser; 40. Conceptualizing time in terms of space: experimental evidence Tom Gijssels and Daniel Casasanto; 41. Discovering spatiotemporal concepts in discourse Thora Tenbrink.
£56.16
