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 Naïve 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.