PATTERN

RECOGNITION

THIRD EDITION

 

SERGIOS THEODORIDIS

KONSTANTINOS KOUTROUMBAS

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 and computer science.The authors have 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. A companion website with a number of demonstrations is also available to aid the reader in gaining practical experience with the techniques and the associated algorithms.

This edition covers 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 techniques and algorithms.

KEY FEATURES
- Up-to-date results on support vector machines including v-SVM’ s and their geometric interpretation.
- Classifier combination techniques including the Boosting approach.
- Feature generation for image analysis, speech recognition and audio classification.
- Up-to-date material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as data-mining and bioinformatics.
- Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification.