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Essentials Of Pattern Recognition
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Book Synopsis Essentials of Pattern Recognition by : Jianxin Wu
Download or read book Essentials of Pattern Recognition written by Jianxin Wu and published by Cambridge University Press. This book was released on 2020-11-19 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.
Book Synopsis Essentials of Pattern Recognition by : Jianxin Wu
Download or read book Essentials of Pattern Recognition written by Jianxin Wu and published by Cambridge University Press. This book was released on 2020-11-19 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.
Book Synopsis Fundamentals of Pattern Recognition and Machine Learning by : Ulisses Braga-Neto
Download or read book Fundamentals of Pattern Recognition and Machine Learning written by Ulisses Braga-Neto and published by Springer Nature. This book was released on 2020-09-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.
Book Synopsis Image Processing and Pattern Recognition by : Frank Y. Shih
Download or read book Image Processing and Pattern Recognition written by Frank Y. Shih and published by John Wiley & Sons. This book was released on 2010-05-03 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.
Book Synopsis Pattern Recognition by : Sergios Theodoridis
Download or read book Pattern Recognition written by Sergios Theodoridis and published by Elsevier. This book was released on 2003-05-15 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms. *Approaches pattern recognition from the designer's point of view *New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere *Supplemented by computer examples selected from applications of interest
Book Synopsis Pattern Recognition in Industry by : Phiroz Bhagat
Download or read book Pattern Recognition in Industry written by Phiroz Bhagat and published by Elsevier. This book was released on 2005-03-30 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage. *Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience in the "real-world" application of cutting-edge technology for competitive advantage in industry. Two wave fronts are upon us today: we are being bombarded by an enormous amount of data, and we are confronted by continually increasing technical and business advances. Ideally, the endless stream of data should be one of our major assets. However, this potential asset often tends to overwhelm rather than enrich. Competitive advantage depends on our ability to extract and utilize nuggets of valuable knowledge and insight from this data deluge. The challenges that need to be overcome include the under-utilization of available data due to competing priorities, and the separate and somewhat disparate existing data systems that have difficulty interacting with each other. Conventional approaches to formulating models are becoming progressively more expensive in time and effort. To impart a competitive edge, engineering science in the 21st century needs to augment traditional modelling processes by auto-classifying and self-organizing data; developing models directly from operating experience, and then optimizing the results to provide effective strategies and operating decisions. This approach has wide applicability; in areas ranging from manufacturing processes, product performance and scientific research, to financial and business fields. This monograph explores pattern recognition technology, and its concomitant role in extracting useful knowledge to build technical and business models directly from data, and in optimizing the results derived from these models within the context of delivering competitive industrial advantage. It is not intended to serve as a comprehensive reference source on the subject. Rather, it is based on first-hand experience in the practice of this technology: its development and deployment for profitable application in industry. The technical topics covered in the monograph will focus on the triad of technological areas that constitute the contemporary workhorses of successful industrial application of pattern recognition. These are: systems for self-organising data; data-driven modelling; and genetic algorithms as robust optimizers. "Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage. Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience in the "real-world" application of cutting-edge technology for competitive advantage in industry.
Book Synopsis Matrix Methods in Data Mining and Pattern Recognition by : Lars Elden
Download or read book Matrix Methods in Data Mining and Pattern Recognition written by Lars Elden and published by SIAM. This book was released on 2007-07-12 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.
Book Synopsis Neural Networks for Applied Sciences and Engineering by : Sandhya Samarasinghe
Download or read book Neural Networks for Applied Sciences and Engineering written by Sandhya Samarasinghe and published by CRC Press. This book was released on 2016-04-19 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in
Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop
Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Book Synopsis Pattern Recognition and Neural Networks by : Brian D. Ripley
Download or read book Pattern Recognition and Neural Networks written by Brian D. Ripley and published by Cambridge University Press. This book was released on 2007 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.
Book Synopsis Pattern Recognition by : Mikhail Moiseevich Bongard
Download or read book Pattern Recognition written by Mikhail Moiseevich Bongard and published by Spartan Publications. This book was released on 1970 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Russian physicist and computer scientist M. M. Bongard presents his ideas on how to use computers to study the process of pattern recognition, which is considered it to be at the foundation of mental processing. The text explains two training programs for recognition and classification: Arithmetic and Geometry. Also introduced in the Appendix are a number of visual puzzles, which have become known as "Bongard Problems" (BP's). BP's are primarily problems of visual categorization, and thus can play an important role in the disciplines of cognitive psychology and cognitive science
Book Synopsis Patterns, Predictions, and Actions: Foundations of Machine Learning by : Moritz Hardt
Download or read book Patterns, Predictions, and Actions: Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Book Synopsis Adaptive Pattern Recognition and Neural Networks by : Yoh-Han Pao
Download or read book Adaptive Pattern Recognition and Neural Networks written by Yoh-Han Pao and published by Addison Wesley Publishing Company. This book was released on 1989 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: A coherent introduction to the basic concepts of pattern recognition, incorporating recent advances from AI, neurobiology, engineering, and other disciplines. Treats specifically the implementation of adaptive pattern recognition to neural networks. Annotation copyright Book News, Inc. Portland, Or.
Book Synopsis Essentials of Machine Olfaction and Taste by : Takamichi Nakamoto
Download or read book Essentials of Machine Olfaction and Taste written by Takamichi Nakamoto and published by John Wiley & Sons. This book was released on 2016-04-18 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Machine Olfaction and Taste This book provides a valuable information source for olfaction and taste which includes a comprehensive and timely overview of the current state of knowledge of use for olfaction and taste machines Presents original, latest research in the field, with an emphasis on the recent development of human interfacing Covers the full range of artificial chemical senses including olfaction and taste, from basic through to advanced level Timely project in that mobile robots, olfactory displays and odour recorders are currently under research, driven by commercial demand
Book Synopsis Introduction to Machine Learning by : Ethem Alpaydin
Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
Book Synopsis Pattern Recognition and Classification in Time Series Data by : Volna, Eva
Download or read book Pattern Recognition and Classification in Time Series Data written by Volna, Eva and published by IGI Global. This book was released on 2016-07-22 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.
Book Synopsis Solving Data Mining Problems Through Pattern Recognition by : Ruby L. Kennedy
Download or read book Solving Data Mining Problems Through Pattern Recognition written by Ruby L. Kennedy and published by Prentice Hall. This book was released on 1997 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is an exploding technology increasingly used in major industries like finance, aerospace, and the medical industry. To truly take advantage of data mining capabilities, one must use and understand pattern recognition techniques. They are addressed in this book along with a tutorial on how to use the accompanying pattern software ("Pattern Recognition Workbench") on the CD-ROM.
