Preface
CHAPTER 1 INTRODUCTION
1.1 Is Pattern Recognition Important?
1.2 Features, Feature Vectors, and Classifiers
1.3 Supervised Versus Unsupervised Pattern Recognition
1.4 Outline of the Book
CHAPTER 2 CLASSIFIERS BASED ON BAYES DECISION THEORY
2.1 Introduction
2.2 Bayes Decision Theory
2.3 Discriminant Functions and Dwcision Surfaces
2.4 Bayesian Classification for Normal Distributions
2.5 Estimation of Unknown Probability Density Functions
2.6 The Nearest Neighbor Rule
2.7 Bayesian Networks
CHAPTER 3 LINEAR CLASSIFIERS
3.1 Introdutcion
3.2 linear Discriminant Functions and Decision Hyperplanes
3.3 The Percptron Algorithm
3.4 Least Squares Mwethods
3.5 Mean Square Estimation Revisited
3.6 Logistic Discrimination
3.7 Support Vector Machines
CHAPTER 4 IONLINEAR CLASSIFIERS
4.1 Introduction
4.2 The XOR Problem
4.3 The Two-Layer Perceptron
4.4 Three-Layer Perceptons
4.5 Algorithms Based on Exact Classification of the Training Set
4.6 The Backpropagation Algorithm
4.7 Variations on the Backpropagation Theme
4.8 The Cost Function Choice
4.9 Choice of the Network Size
4.10 A Simulation Example
4.11 Networks With Weight Sharing
4.12 Generalized Linear Classifiers
4.13 Capacity of the l-Dimensional Space in Linear Dichotomies
4.14 Polynomial Classifiers
4.15 Radial Basis Function Networks
4.16 Universal Approximatiors
4.17 Support Vector Machines: The Nonlinear Case
CHAPTER 5 FEATURE SELECTION
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CHAPTER 6 FEATURE GENERATION Ⅰ：LINEAR TRANSFORMS
CHAPTER 7 FEATURE GENERATION Ⅱ
CHAPTER 8 TEMPLATE MATCHING
CHAPTER 9 CONTEXT-DEPENDENT CLASIFICATION
CHAPTER 10 SYSTEM EVALUATION
CHAPTER 11 CLUSTERING：BASIC CONCEPTS
CHAPTER 12 CLUSTERING ALGORITHMSⅠ：SEQUENTIAL ALGORITHMS
CHAPTER 13 CLUSTERING ALGORITHMSⅡ：HIERARCHICAL ALGORITHMS
CHAPTER 14 CLUSTERING ALGORITHMSⅢ：SCHEMES BASED ON FUNCTION OPTIMIZATION
CHAPTER 15 CLUSTERING ALGORITHMSⅣ
CHAPTER 16 CLUSTER VALIDITY
Appendix A Hints form Probability and Statistics
Appendix B Linear Algebra Basics
