Statistical Pattern Recognition
- Introduction
- Bayesian decision theory, Minimum Error Rate classification, Normal Density, Discriminant functions for normal density, Discrete Features, Missing and Noisy Features
- Maximum Likelihood Estimation, Bayesian Estimations
- Nonparametric Techniques, Nearest Neighbor Classifiers, Fisher Classifiers
- Linear Discriminant Functions, Two categories linear classifiers, Perceptrons, Relaxation Procedures, Support Vector Machines, Multicategory Generalizations
- Tree-based methods, classification trees, Multivariate adaptive regression splines
- Estimating and Comparing classifiers, Parametric Models, Cross-Validation, Combining Classifiers, Component Classifiers with Discriminant Functions, Component Classifiers without Discriminant Functions
- Feature selection and extraction, feature selection, feature selection criterion, search algorithm for feature selection, branch and bound procedures
- Clustering, Hierarchical Methods, single-link method, complete-link method, sum of square method, general agglomerative algorithm, properties of a hierarchical classification