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 Decision Surfaces 2.4 Bayesian Classification for Normal Distributions 2.5 Estimation of Unknown Probability Density Functions 2.6 The Nearest Neighbor Rule CHAPTER 3 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 Support Vector Machines CHAPTER 4 NONLINEAR CLASSIFIERS 4.1 Introduction 4.2 The XOR Problem 4.3 The Two-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 in Linear Dichotomies 4.14 Polynomial Classifiers 4.15 Radial Basis Function Networks 4.16 Universal Approximators 4.17 Support Vector Machines: The Nonlinear Case 4.18 Decision Trees 4.19 Discussion CHAPTER 5 FEATURE SELECTION 5.1 Introduction 5.2 Preprocessing 5.3 Feature Selection Based on Statistical Hypothesis Testing 5.4 The Receiver Operating Characteristics CROC Curve 5.5 Class Separability Measures 5.6 Feature Subset Selection 5.7 Optimal Feature Generation 5.8 Neural Networks and Feature Generation/Selection 5.9 A Hint on the Vapnik--Chemovenkis Learning Theory CHAPTER 6 FEATURE GENERATION I: LINEAR TRANSFORMS 6.1 Introduction 6.2 Basis Vectors and Images 6.3 The Karhunen-Loeve Transform 6.4 The Singular Value Decomposition 6.5 Independent Component Analysis 6.6 The Discrete Fourier Transform (DFT) 6.7 The Discrete Cosine and Sine Transforms 6.8 The Hadamard Transform 6.9 The Haar Transform 6.10 The Haar Expansion Revisited 6.11 Discrete Time Wavelet Transform (DTWT) 6.12 The Multiresolution Interpretation 6.13 Wavelet Packets 6.14 A Look at Two-Dimensional Generalizations 6.15 Applications CHAPTER 7 FEATURE GENERATION II 7.1 Introduction 7.2 Regional Features 7.3 Features for Shape and Size Characterization 7.4 A Glimpse at Fractals CHAPTER 8 TEMPLATE MATCHING 8.1 Introduction 8.2 Measures Based on Optimal Path Searching Techniques 8.3 Measures Based on Correlations 8.4 Deformable Template Models CHAPTER 9 CONTEXT-DEPENDENT CLASSIFICATION 9.1 Introduction 9.2 The Bayes Classifier 9.3 Markov Chain Models 9.4 The Viterbi Algorithm 9.5 Channel Equalization 9.6 Hidden Markov Models 9.7 Training Markov Models via Neural Networks 9.8 A discussion of Markov Random Fields CHAPTSR 10 SYSTEM EVALUATION 10.1 Introduction 10.2 Error Counting Approach 10.3 Exploiting the Finite Size of the Data Set 10.4 A Case Study From Medical Imaging CHAPTER 11 CLUSTERING: BASIC CONCEPTS 11.1 Introduction 11.2 Proximity Measures CHAPTER 12 CLUSTERING ALGORITHMS I: SEQUENTIAL ALGORITHMS 12.1 Introduction 12.2 Categories of Clustering Algorithms 12.3 Sequential Clustering Algorithms 12.4 A Modification of BSAS 12.5 A Two-Threshold Sequential Scheme 12.6 Refinement Stages 12.7 Neural Network Implementation CHAPTER 13 CLUSTERING ALGORITHMS II: HIERARCHICAL ALGORITHMS 13.1 Introduction 13.2 Agglomerative Algorithms 13.3 The Cophenetic Matrix 13.4 Divisive Algorithms 13.5 Choice of the Best Number of Clusters CHAPTER 14 CLUSTERING ALGORITHMS III: SCHEMES BASED ON FUNCTION OPTIMIZATION 14.1 Introduction 14.2 Mixture Decomposition Schemes 14.3 Fuzzy Clustering Algorithms 14.4 Possibilistic Clustering 14.5 Hard Clustering Algorithms 14.6 Vector Quantization CHAPTER 15 CLUSTERING ALGORITHMS IV 15.1 Introduction 15.2 Clustering Algorithms Based on Graph Theory 15.3 Competitive Learning Algorithms 15.4 Branch and Bound Clustering Algorithms 15.5 Binary Morphology Clustering Algorithms (BMCAs) 15.6 Boundary Detection Algorithms 15.7 Valley-Seeking Clustering Algorithms 15.8 Clustering Via Cost Optimization (Revisited) 15.9 Clustering Using Genetic Algorithms 15.10 Other Clustering Algorithms CHAPTER 16 CLUSTER VALIDITY 16.1 Introduction 16.2 Hypothesis Testing Revisited 16.3 Hypothesis Testing in Cluster Validity 16.4 Relative Criteria 16.5 Validity of Individual Clusters 16.6 Clustering Tendency Appendix A Hints from Probability and Statistics Appendix B Linear Algebra Basics Appendix C Cost Function Optimization Appendix D Basic Definitions from Linear Systems Theory Index
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