Ebook: Pattern Classification

This edition has been completely revised, enlarged and formatted in two colours. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples. Introduction -- Bayesian decision theory -- Maximum-likelihood and Bayesian parameter estimation -- Nonparametric techniques -- Linear discriminant functions -- Multilayer neural networks -- Stochastic methods -- Nonmetric methods -- Algorithm-independent machine learning -- Unsupervised learning and clustering -- Mathematical foundations