Indice

Bibliografía: p. 711-728

The field of pattern recognition has undergone substantial development over the last ten years, as Bayesian methods have become mainstream, while graphical models have emerged as a common framework for describing and applying probabilistic models. This book builds on the authors̕ very popular Neural Networks for Pattern Recognition (1995), exploring Bayesian methods for pattern recognition, and new topics such as kernel methods and temporal models. Data sets provide running examples illustrating the application of many of the algorithms discussed. The author incorporates recent developments while providing a solid grounding in the basic concepts of pattern recognition and machine learning.

Índice: Preface. Mathematical notation. Contents. Introduction. .Example: Polynomial Curve Fitting. 1.2 Probability Theory. 1.2.1 Probability densities. 1.2.2 Expectations and. . covariances. 1.2.3 Bayesian probabilities. 1.2.4 The Gaussian distribution. 1.2.5 Curve fitting re-visited. 1.2.6 Bayesian curve fitting. 1.3 Model Selection. 1.4 The Curse of Dimensionality. 1.5 Decision Theory. 1.5.1 Minimizing the misclassification rate. 1.5.2 Minimizing the expected loss. 1.5.3 The reject option. 1.5.4 Inference and decision. 1.5.5 Loss functions for regression. 1.6 Information Theory. 1.6.1 Relative entropy and mutua... Etc.