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.
Chapter 1 Introduction
Chapter 2 Classifiers Based on Bayes Decision Theory
Cahpter 3 Linear Classifiers
Chapter 4 Nonlinear Classifiers
Chapter 5 Feature Selection
Chapter 6 Feature Generation I : Data Transformation and Dimensionality Reduction
Chapter 7 Feature Generation II
Chapter 8 Template Matching
Chapter 9 Context-Dependent Classification
Chapter 10 Supervised Learning : The Epilogue
etc.