Preface Chapter 1‎.‎ Classifiers Based on Bayes Decision Theory 1.1 Introduction 1.2 Bayes Decision Theory 1.3 The Gaussian Probability Density Function 1.4 Minimum Distance Classifiers 1.4‎.‎1 The Euclidean Distance Classifier 1.4‎.‎2 The Mahalanobis Distance Classifier 1.4‎.‎3 Maximum Likelihood Parameter Estimation of Gaussian pdfs 1.5 Mixture Models 1.6 The Expectation‎-Maximization Algorithm 1.7 Parzen Windows 1.8 k‎-Nearest Neighbor Density Estimation 1.9 The Naive Bayes Classifier 1.10 The Nearest Neighbor Rule Chapter 2‎.‎ Classifiers Based on Cost Function Optimization 2.1 Introduction 2.2 The Perceptron Algorithm 2.2‎.‎1 The Online Form of the Perceptron Algorithm 2.3 The Sum of Error Squares Classifier 2.3‎.‎1 The Multiclass LS Classifier 2.4 Support Vector Machines‎:‎ The Linear Case 2.4‎.‎1 Multiclass Generalizations 2.5 SVM‎:‎ The Nonlinear Case 2.6 The Kernel Perceptron Algorithm 2.7 The AdaBoost Algorithm 2.8 Multilayer Perceptrons Chapter 3‎.‎ Data Transformation‎:‎ Feature Generation and Dimensionality Reduction 3.1 Introduction 3.2 Principal Component Analysis 3.3 The Singular Value Decomposition Method 3.4 Fisher‎'‎s Linear Discriminant Analysis 3.5 The Kernel PCA 3.6 Laplacian Eigenmap Chapter 4‎.‎ Feature Selection 4.1 Introduction 4.2 Outlier Removal 4.3 Data Normalization 4.4 Hypothesis Testing‎:‎ The t‎-Test 4.5 The Receiver Operating Characteristic Curve 4.6 Fisher‎'‎s Discriminant Ratio 4.7 Class Separability Measures 4.7‎.‎1 Divergence 4.7‎.‎2 Bhattacharyya Distance and Chernoff Bound 4.7‎.‎3 Measures Based on Scatter Matrices 4.8 Feature Subset Selection 4.8‎.‎1 Scalar Feature Selection 4.8‎.‎2 Feature Vector Selection Chapter 5‎.‎ Template Matching 5.1 Introduction 5.2 The Edit Distance 5.3 Matching Sequences of Real Numbers 5.4 Dynamic Time Warping in Speech Recognition Chapter 6‎.‎ Hidden Markov Models 6.1 Introduction 6.2 Modeling 6.3 Recognition and Training Chapter 7‎.‎ Clustering 7.1 Introduction 7.2 Basic Concepts and Definitions 7.3 Clustering Algorithms 7.4 Sequential Algorithms 7.4‎.‎1 BSAS Algorithm 7.4‎.‎2 Clustering Refinement 7.5 Cost Function Optimization Clustering Algorithms 7.5‎.‎1 Hard Clustering Algorithms 7.5‎.‎2 Nonhard Clustering Algorithms 7.6 Miscellaneous Clustering Algorithms 7.7 Hierarchical Clustering Algorithms 7.7‎.‎1 Generalized Agglomerative Scheme 7.7‎.‎2 Specific Agglomerative Clustering Algorithms 7.7‎.‎3 Choosing the Best Clustering Appendix References Index