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