Pattern Recognition

Sergios Theodoridis, Konstantinos Koutroumbas

836 pages, parution le 16/12/2005

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Résumé

A classic -- offering comprehensive and unified coverage with a balance between theory and practice!

Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition and audio classification, communications, computer-aided diagnosis, data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information.

Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms.

This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering.

Sommaire

Introduction
Classifiers Based on Bayes Decision Theory
Linear Classifiers
Nonlinear Classifiers
Feature Selection
Feature Generation I
Feature Generation II
Template Matching
Context-Dependant Classification
System Evaluation
Clustering: Basic Concepts
Clutering Algorithms I (Sequential)
Clustering Algorithms II (Hierarchical
Clustering Algorithms III (Functional Optimization)
Clustering Algorithms IV (Graph Theory)
Cluster Validity

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Academic Press
Auteur(s)	Sergios Theodoridis, Konstantinos Koutroumbas
Parution	16/12/2005
Nb. de pages	836
Format	16 x 23,5
Couverture	Relié
Poids	1428g
Intérieur	Noir et Blanc
EAN13	9780123695314
ISBN13	978-0-12-369531-4