Statistical pattern recognition /

• Main Author: Webb, A. R. (Andrew R.)
• Format: eBook
• Language: English
• Published: West Sussex, England ; New Jersey : Wiley, �2002.
• Edition: 2nd ed.
• Subjects: Pattern perception > Statistical methods.; Perception des structures > M�ethodes statistiques.; COMPUTERS > Optical Data Processing.; Discriminantanalyse.; Clusteranalyse.; Classificatie.; Reconhecimento de padr�oes.; Computa�c�ao gr�afica.; Computational and graphical statistics.; Electronic books.
• Physical Description: 1 online resource (xviii, 496 pages) : illustrations
• Format: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
• Bibliography: Includes bibliographical references (pages 459-490) and index.
• ISBN: 0470854782; 9780470854785; 0470845139; 9780470845134; 0470845147; 9780470845141; 0470854774; 9780470854778

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. * Provides a self-contained introduction to statistical pattern recognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. * Each section concludes with a description of the applications that have been addressed and with further developments of the theory. * Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.