Pattern Classification

Author: Richard O. Duda
Publisher: John Wiley & Sons
ISBN: 9781118586006
Release Date: 2012-11-09
Genre: Technology & Engineering

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Support Vector Machines for Pattern Classification

Author: Shigeo Abe
Publisher: Springer Science & Business Media
ISBN: 1849960984
Release Date: 2010-07-23
Genre: Technology & Engineering

A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.

Pattern Classification and Scene Analysis

Author: Richard O. Duda
Publisher: Wiley
ISBN: 0471223611
Release Date: 1973-02-09
Genre: Reference

Introduction to Mathematical Techniques in Pattern Recognition by Harry C. Andrews This volume is one of the first cohesive treatments of the use of mathematics for studying interactions between various recognition environments. It brings together techniques previously scattered throughout the literature and provides a concise common notation that will facilitate the understanding and comparison of the many aspects of mathematical pattern recognition. The contents of this volume are divided into five interrelated subject areas: Feature Selection, Distribution Free Classification, Statistical Classification, Nonsupervised Learning, and Sequential Learning. Appendices describing specific aspects of feature selection and extensive reference and bibliographies are included. 1972 253 pp. Threshold Logic and its Applications by Saburo Muroga This is the first in-depth exposition of threshold logic and its applications using linear programming and integer programming as optimization tools. It presents threshold logic as a unified theory of conventional simple gates, threshold gates and their networks. This unified viewpoint explicitly reveals many important properties that were formerly concealed in the framework of conventional switching theory (based essentially on and, or and not gates). 1971 478 pp. Knowing and Guessing A Quantitative Study of Inference and Information By Satosi Watanabe This volume presents a coherent theoretical view of a field now split into different disciplines: philosophy, information science, cybernetics, psychology, electrical engineering, and physics. The target of investigation is the cognitive process of knowing and guessing. In contrast to traditional philosophy, the approach is quantitative rather than qualitative. The study is formal in the sense that the author is not interested in the contents of knowledge or the physiological mechanism of the process of knowing. "The author’s style is lucid, his comments are illuminating. The result is a fascinating book, which will be of interest to scientists in many different fields." — Nature 1969 592 pp.

Pattern classification

Author: Jürgen Schürmann
Publisher: Wiley-Interscience
ISBN: 0471135348
Release Date: 1996-03-15
Genre: Computers

PATTERN CLASSIFICATION a unified view of statistical and neural approaches The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable. Offering a lucid presentation of complex applications and their algorithms, Pattern Classification is an invaluable resource for researchers, engineers, and graduate students in this rapidly developing field.

Pattern Recognition and Classification

Author: Geoff Dougherty
Publisher: Springer Science & Business Media
ISBN: 9781461453239
Release Date: 2012-10-28
Genre: Computers

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

Pattern Recognition

Author: Sergios Theodoridis
Publisher: Academic Press
ISBN: 0080949126
Release Date: 2008-11-26
Genre: Computers

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques · Many more diagrams included--now in two color--to provide greater insight through visual presentation · Matlab code of the most common methods are given at the end of each chapter. · More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869). Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.

A Probabilistic Theory of Pattern Recognition

Author: Luc Devroye
Publisher: Springer Science & Business Media
ISBN: 9781461207115
Release Date: 2013-11-27
Genre: Mathematics

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.

Introduction to Pattern Recognition

Author: Sergios Theodoridis
Publisher: Academic Press
ISBN: 0080922759
Release Date: 2010-03-03
Genre: Computers

Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-life data sets in imaging and audio recognition Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

Neural Networks for Pattern Recognition

Author: Albert Nigrin
Publisher: MIT Press
ISBN: 0262140543
Release Date: 1993
Genre: Computers

In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before.

Pattern Recognition

Author: M. Narasimha Murty
Publisher: Springer Science & Business Media
ISBN: 0857294954
Release Date: 2011-05-25
Genre: Computers

Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate and postgraduate students of computer science and allied disciplines.

Pattern classification

Author: Richard O. Duda
Publisher: Wiley-Interscience
ISBN: UOM:39015050303265
Release Date: 2001
Genre: Computers

This edition has been completely revised, enlarged and formatted in two colour. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples, exercises and a solutions manual.

Pattern Recognition

Author: Sankar K. Pal
Publisher: World Scientific
ISBN: 981238653X
Release Date: 2001
Genre: Computers

This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource. Contents: Pattern Recognition: Evolution of Methodologies and Data Mining (A Pal & S K Pal); Adaptive Stochastic Algorithms for Pattern Classification (M A L Thathachar & P S Sastry); Shape in Images (K V Mardia); Decision Trees for Classification: A Review and Some New Results (R Kothari & M Dong); Syntactic Pattern Recognition (A K Majumder & A K Ray); Fuzzy Sets as a Logic Canvas for Pattern Recognition (W Pedrycz & N Pizzi); Neural Network Based Pattern Recognition (V David Sanchez A); Networks of Spiking Neurons in Data Mining (K Cios & D M Sala); Genetic Algorithms, Pattern Classification and Neural Networks Design (S Bandyopadhyay et al.); Rough Sets in Pattern Recognition (A Skowron & R Swiniarski); Automated Generation of Qualitative Representations of Complex Objects by Hybrid Soft-Computing Methods (E H Ruspini & I S Zwir); Writing Speed and Writing Sequence Invariant On-line Handwriting Recognition (S-H Cha & S N Srihari); Tongue Diagnosis Based on Biometric Pattern Recognition Technology (K Wang et al.); and other papers. Readership: Graduate students, researchers and academics in pattern recognition.

Pattern Classification

Author: Duda
Publisher: John Wiley & Sons
ISBN: 8126511168
Release Date: 2006
Genre: Pattern recognition systems

Market_Desc: · Senior and Graduate level courses· Professionals in Computer Science and Electrical Engineering· Researchers in speech recognition, optical character recognition, signal analysis, image processing Special Features: The book· Provides an inexpensive MATLAB toolbox for the main algorithms in pattern classification· Contains all the algorithms in Pattern Classification, 2E as well as supporting algorithms for data generation and visualization· Uses the same terminology as Patten Classification, 2e· Contains step-by-step worked examples· Accompanied by software containing all algorithms in Pattern Classification, 2e, indexed to that best-selling title· Software code is self-annotating so users can easily navigate, understand, and modify the code About The Book: The book provides an inexpensive MATLAB toolbox for the main algorithms in pattern classification. It contains supporting algorithms for data generation and visualization and contains step-by-step worked examples.

Computational Intelligence Paradigms in Advanced Pattern Classification

Author: Marek R. Ogiela
Publisher: Springer
ISBN: 9783642240492
Release Date: 2012-01-13
Genre: Computers

This monograph presents selected areas of application of pattern recognition and classification approaches including handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, advanced image filtration and intelligent multi-object labelling and classification. It is directed to the scientists, application engineers, professors, professors and students will find this book useful.