Pattern recognition Books

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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.

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Book SynopsisThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.Trade Review'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM ReviewTable of Contents1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.

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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

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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.

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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.

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Book SynopsisThe 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 notatioTrade Review'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'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'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'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'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'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'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'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 TechnologyTable of ContentsPreface; 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.

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Book SynopsisThis accessible, comprehensive guide is aimed at students, practitioners, engineers, and users. The emphasis is on building robust, responsible machine learning products incorporating meaningful metrics, rigorous statistical analysis, fair training sets, and explainability. Implementations in Python and sklearn are available on the book's website.

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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.

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Book SynopsisCognitive 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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a huge range and FREE tracked UK delivery on ALL orders.

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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.

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Book SynopsisThis 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.An Intuitive Exploration of Artificial Intelligence 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.The 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.Table of ContentsPart 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.

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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.

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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.

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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

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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.

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Book Synopsis"Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics.The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering.Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.Trade ReviewFrom the reviews:“This book is a collection of 31 scientific papers organized in four main sections: ‘Pattern recognition and Machine Intelligence’, ‘Computer Vision and Image Processing’, ‘Face Recognition and Forensics’ and ‘Biometrics Authentication’. These chapters cover a broad domain making the book appealing to a large group of specialists.” (Leon Todoran, IAPR Newsletter, Vol. 34 (4), October, 2012)Table of ContentsIntroductions and Editorial.- Fingerprint Analysis and Recognition.- Handwriting Analysis and Extraction.- Symbolic Factorial Discriminant Analysis for Face Recognition.- Noniterative 3D Face Reconstruction Based on Photometric Stereo.- Multimodal Biometrics by Face and Hand Images.- Signature Verification Technique Using Data Glove.- Performance Comparisons of Facial Expression Recognition in JAFFE Database.- Inverse Biometrics for Mouse Dynamics.- User Friendly Identification with Stepping.- Colored Faces and Facial Features Extractions.- Comparison of ROC and Likelihood Decision Methods in Fingerprint Analysis and Recognition.- Facial Metamorphosis for Biometric Applications.- Genetic Algorithm Using Fingerprint and Iris Biometrics.- Fingerprint Asymmetry Measures.- Application Iris Recognition Using Classifier Combination.- Development of Handwriting Recognition of Whiteboard Notes.- Recent Development of Speech Analysis and Recognition in Biometrics.- Case Studies of Ambiguities of Biometrics.- Conclusions, Open Problems, and Future Research.- Other Related Subtopics by Prominent Professionals.

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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.

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Book SynopsisWith 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 onliTrade Review'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'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'The book is a valuable tool for both beginning and advanced researchers in the field.' Catalin Stoean, zbMATHTable of ContentsPart 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.

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Book SynopsisThis 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.

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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.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisData stealing is a major concern on the internet as hackers and criminals have begun using simple tricks to hack social networks and violate privacy. Cyber-attack methods are progressively modern, and obstructing the attack is increasingly troublesome, regardless of whether countermeasures are taken. The Dark Web especially presents challenges to information privacy and security due to anonymous behaviors and the unavailability of data. To better understand and prevent cyberattacks, it is vital to have a forecast of cyberattacks, proper safety measures, and viable use of cyber-intelligence that empowers these activities.Dark Web Pattern Recognition and Crime Analysis Using Machine Intelligence discusses cyberattacks, security, and safety measures to protect data and presents the shortcomings faced by researchers and practitioners due to the unavailability of information about the Dark Web. Attacker techniques in these Dark Web environments are highlighted, along with intrusion detection practices and crawling of hidden content. Covering a range of topics such as malware and fog computing, this reference work is ideal for researchers, academicians, practitioners, industry professionals, computer scientists, scholars, instructors, and students.

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Book SynopsisOriginal edition reissued in 2023 with new cover. This is a print on demand title and is not held in stock. The delivery leadtimes will be longer. Data mining is about finding patterns hidden inside data. It’s how the supermarket knows when your kids leave home or when your granny comes to visit; it’s how the credit card company detects fraud and how your insurance company decides whether to cover you. Searching For Patterns guides you through the techniques used to do this and the people behind them, in an easily accessible and entertaining way. Data mining is mathematical, but all the maths in this book has been kept separate from the main text, so you can skip it if you want.

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Book SynopsisBiometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.Table of ContentsPart I: Introduction Introduction Statistical Background Part II: Primary Matching and Classification Measures False Non-Match Rate False Match Rate Receiver Operating Characteristic Curve and Equal Error Rate Part III: Biometric Specific Measures Failure to Enrol Failure to Acquire Part IV: Additional Topics and Appendices Additional Topics and Discussion Tables

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Book SynopsisSmart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure.This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems.This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.Table of ContentsPart I: Sensing and Activity Monitoring Multi-Resident Activity Monitoring in Smart Homes Through Non-Wearable Non-Intrusive SensorsSon N. Tran and Qing Zhang and Vanessa Smallbon and Mohan Karunanithi Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric VideosGeorgios Kapidis, Ronald W. Poppe, Elsbeth A. van Dam, Remco C. Veltkamp, and Lucas P. J. J. Noldus A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-LivingGiovanni Schiboni, Fabio Wasner, and Oliver AmftSaving Energy on EMG-Monitoring Eyeglasses for Free-Living Eating Event Spotting Using Adaptive Duty-CyclingGiovanni Schiboni and Oliver Amft Indoor Localisation with WiFi Fingerprinting Based on a Convolutional Neural NetworkZumin Wang Unobtrusive Sensing to Assist with Post-Stroke RehabilitationChris Nugent Part II: Activity Recognition and Behaviour Analysis Energy-Based Decision Engine for Household Human Activity RecognitionAnastasios Vafeiadis, Thanasis Vafeiadis, Stelios Zikos, Stelios Krinidis, Konstantinos Votis, Dimitrios Giakoumis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Liming Chen, and Raouf Hamzaoui Distributed Context Recognition, a Systematic ReviewUmar Ahmad and Luis Lopera Exercise Type Recognition Using Transfer LearningHossein Malekmohamadi Meta-Intelligence for Behaviour RecognitionXiaodong Liu and Qi Liu Part III: User Needs and Personalisation A Conceptual Framework for Adaptive User Interfaces for Older AdultsEduardo Machado, Deepika Singhy, Federico Cruciani, Liming Chen, Sten Hankey, Fernando Salvago, Johannes Kropf, and Andreas HolzingerStudying the Technological Barriers and Needs of People with Dementia: A Quantitative StudyNikolaos Liappas, Rebeca Isabel García-Betances, José Gabriel Teriús-Padrón, and María Fernanda Cabrera-Umpiérrez Adaptive Service Robot Behaviours Based on User Mood: Towards Better Personalized Support of MCI Patients at HomeDimitrios Giakoumis, Georgia Peleka, Manolis Vasileiadis, Ioannis Kostavelis, and Dimitrios Tzovaras Part IV: Ambient Assisted Living Solutions Towards Cognitive Assisted LivingClaudia Steinberger and Judith Michael Towards Self-Management of Chronic Diseases in Smart HomesJosé G. Teriús-Padrón, Georgios Kapidis, Sarah Fallmann, Erinc Merdivan, Sten Hanke, Rebeca I. García-Betances, and María Fernanda Cabrera-UmpiérrezA Deep Learning Approach for Privacy Preservation in Assisted LivingIsmini Psychoula, Erinc Merdivany, Deepika Singhy, Liming Chen, Feng Chen, Sten Hankey, Johannes Kropfy, Andreas Holzingerx, and Matthieu GeistTowards Socially Assistive Robots for the Elderly: An End-to-End Object Search FrameworkMohammad Reza Loghmani, Timothy Patten and Markus VinczeModelling Activities of Daily Living with Petri NetsMatias Garcia-Constantino, Alexandros Konios and Chris Nugent Calculus of Context-Aware Ambients for Assisted Living System ModellingFrancois Siewe

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Book Synopsis.- Cybersecurity based on Graph models..- A Modular Triple Exchange Co-learning Framework for Anomaly Detection in Scarcely Labeled Graph Data..- Advanced Malware Detection in Code Repositories Using Graph Neural Network..- Resistance Distance Guided Node Injection Attack on Graph Neural Network..- Graph based bioinformatics..- Gene Co-Expression Networks Are Poor Proxies for Expert-Curated Gene Regulatory Networks..- Graph Neural Network Based on Molecular and Pharmacophoric Features for Drug Design Applications..- Graph-Based Representations of Almost Constant Graphs for Nanotoxicity Prediction..- Label Modulated Dynamic Graph Convolution for Subcellular Structure Segmentation from Nanoscopy Image..- Insights on Using Graph Neural Networks for Sulcal Graphs Predictive Models..- Graph Neural Networks for Multimodal Brain Connectivity Analysis in Multiple Sclerosis..- Graph similarities and graph patterns..-  A Geometric Perspective on Graph Similarity Learning using Convex Hulls..- VF-GPU: Exploiting Parallel GPU Architectures to Solve Subgraph Isomorphis..- Grammatical Path Network: Unveiling Cycles Through Path Computation..- Deep QMiner: Towards a generalized DeepQ-Learning Approach for Graph Pattern Mining..- GNN: shortcomings and solutions..- An Empirical Investigation of Shortcuts in Graph Learning..-  A General Sampling Framework for Graph Convolutional Network Training..- Fusion of GNN and GBDT Models for Graph and Node Classification..- Harnessing GraphSAGE for Learning Representations of Massive Transactional networks..- Entropy-Guided Graph Clustering via Rényi Optimization..- Graph learning and computer vision..- Exploring a Graph Regression Problem in River Networks..- Saliency Matters: from nodes to objects..- Hierarchical super-pixels graph neural networks for image semantic segmentation..- Lifting some Secrets about Contrast Pyramids..- An Evolution Equation Involving the Generalized Biased Infinity Laplacian on Graphs..- Doc2Graph-X: A Multilingual Graph-Based Framework for Form Understanding..- VisHubGAT: Visible Connectivity and Hub Nodes for Multimodal Entity Extraction.

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