Pattern Recognition and Classification

Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 Overview -- 1.2 Classification -- 1.3 Organization of the Book -- Bibliography -- Exercises -- Chapter 2 Classification -- 2.1 The Classification Process -- 2.2 Features -- 2.3 Training and Learning -- 2.4 Supervised Learning and Algorithm Selection -- 2.5 Approaches to Classification -- 2.6 Examples -- 2.6.1 Classification by Shape -- 2.6.2 Classification by Size -- 2.6.3 More Examples -- 2.6.4 Classification of Letters -- Bibliography -- Exercises -- Chapter 3 Non-Metric Methods -- 3.1 Introduction -- 3.2 Decision Tree Classifier -- 3.2.1 Information, Entropy and Impurity -- 3.2.2 Information Gain -- 3.2.3 Decision Tree Issues -- 3.2.4 Strengths and Weaknesses -- 3.3 Rule-Based Classifier -- 3.4 Other Methods -- Bibliography -- Exercises -- Chapter 4 Statistical Pattern Recognition -- 4.1 Measured Data and Measurement Errors -- 4.2 Probability Theory -- 4.2.1 Simple Probability Theory -- 4.2.2 Conditional Probability and Bayes' Rule -- 4.2.3 Na??ve Bayes classifier -- 4.3 Continuous Random Variables -- 4.3.1 The Multivariate Gaussian -- 4.3.2 The Covariance Matrix -- 4.3.3 The Mahalanobis Distance -- Bibliography -- Exercises -- Chapter 5 Supervised Learning -- 5.1 Parametric and Non-Parametric Learning -- 5.2 Parametric Learning -- 5.2.1 Bayesian Decision Theory -- 5.2.2 Discriminant Functions and Decision Boundaries -- 5.2.3 MAP (Maximum A Posteriori) Estimator -- Bibliography -- Exercises -- Chapter 6 Non-Parametric Learning -- 6.1 Histogram Estimator and Parzen Windows -- 6.2 k-Nearest Neighbor (k-NN) Classification -- 6.3 Artificial Neural Networks (ANNs) -- 6.4 Kernel Machines -- Bibliography -- Exercises -- Chapter 7 Feature Extraction and Selection -- 7.1 Reducing Dimensionality -- 7.1.1 Pre-Processing -- 7.2 Feature Selection -- 7.2.1 Inter/Intra-Class Distance -- 7.2.2 Subset Selection -- 7.3 Feature Extraction -- 7.3.1 Principal Component Analysis (PCA) -- 7.3.2 Linear Discriminant Analysis (LDA) -- Bibliography -- Exercises -- Chapter 8 Unsupervised Learning -- 8.1 Clustering -- 8.2 k-Means Clustering -- 8.2.1 Fuzzy c-Means Clustering -- 8.3 (Agglomerative) Hierarchical Clustering -- Bibliography -- Exercises -- Chapter 9 Estimating and Comparing Classifiers -- 9.1 Comparing Classifiers and the No Free Lunch Theorem -- 9.1.2 Bias and Variance -- 9.2 Cross-Validation and Resampling Methods -- 9.2.1 The Holdout Method -- 9.2.2 k-Fold Cross-Validation -- 9.2.3 Bootstrap -- 9.3 Measuring Classifier Performance -- 9.4 Comparing Classifiers -- 9.4.1 ROC curves -- 9.4.2 McNemar's Test -- 9.4.3 Other Statistical Tests -- 9.4.4 The Classification Toolbox -- 9.5 Combining classifiers -- Bibliography -- Chapter 10 Projects -- 10.1 Retinal Tortuosity as an Indicator of Disease -- 10.2 Segmentation by Texture -- 10.3 Biometric Systems -- 10.3.1 Fingerprint Recognition -- 10.3.2 Face Recognition -- Bibliography -- Index.