Preface CHAPTER1 Introduction 1.1 Is Pattern Recognition Important? 1.2 Features, Feature Vectors, and Classifiers 1.3 Supervised, Unsupervised, and Semi-Supervised Learning 1.4 MATLAB Programs 1.5 Outline of The Book CHAPTER2 Classifiers Based on Bayes Decision Theory 2.1 Introduction 2.2 Bayes Decision Theory 2.3 Discriminant Functions and Decision 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 2.8 Problems References CHAPTER3 Linear Classifiers 3.1 Introduction 3.2 Linear Discriminant Functions and Decision Hyperplanes 3.3 The Perceptron Algorithm 3.4 Least Squares Methods 3.5 Mean Square Estimation Revisited 3.6 Logistic Discrimination 3.7 Support Vector Machines 3.8 Problems References CHAPTER 4 Nonlinear Classifiers 4.1 Introduction 4.2 The XOR Problem 4.3 TheTwo-Layer Perceptron 4.4 Three-Layer Perceptrons 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/-Dimensional Space inLinear Dichotomies 4.14 Polynomial Classifiers 4.15 Radial Basis Function Networks 4.16 UniversalApproximators 4.17 Probabilistic Neural Networks 4.18 Support Vector Machines: The Nonlinear Case 4.19 Beyond the SVM Paradigm 4.20 Decision Trees 4.21 Combining Classifiers 4.22 The Boosting Approach to Combine Classifiers 4.23 The Class Imbalance Problem 4.24 Discussion 4.25 Problems References CHAPTER5 Feature Selection 5.1 Introduction 5.2 Preprocessing 5.3 The Peaking Phenomenon 5.4 Feature Selection Based on Statistical Hypothesis Testing 5.5 The Receiver Operating Characteristics (ROC) Curve 5.6 Class Separability Measures 5.7 Feature Subset Selection 5.8 Optimal Feature Generation 5.9 Neural Networks and Feature Generation/Selection 5.10 A Hint On Generalization Theory 5.11 The Bayesian Information Criterion 5.12 Problems References CHAPTER 6 FEATURE GENERATION Ⅰ：LINEAR TRANSFORMS CHAPTER 7 FEATURE GENERATION Ⅱ CHAPTER 8 TEMPLATE MATCHING CHAPTER 9 CONTEXT-DEPENDENT CLASIFICATION CHAPTER10 SYSTEM EVALUATION CHAPTER11 CLUSTERING：BASIC CONCEPTS CHAPTER12 CLUSTERING ALGORITHMSⅠ：SEQUENTIAL ALGORITHMS CHAPTER13 CLUSTERING ALGORITHMSⅡ：HIERARCHICAL ALGORITHMS CHAPTER14 CLUSTERING ALGORITHMSⅢ：SCHEMES BASED ON FUNCTION OPTIMIZATION CHAPTER15 CLUSTERING ALGORITHMSⅣ CHAPTER16 CLUSTER VALIDITY Appendix A Hints form Probability and Statistics Appendix B Linear Algebra Basics
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