1 Introduction and Examples 1.1 How do neural methods differ? 1.2 The patterm recognition task 1.3 Overview of the remaining chapters 1.4 Examples 1.5 Literature2 Statistical Decision Theory 2.1 Bayes rules for known distributions 2.2 Parametric models 2.3 Logistic discrimination 2.4 Predictive classification 2.5 Alternative estimation procedures 2.6 How complex a model do we need? 2.7 Performance assessment 2.8 Computational learning approaches3 Linear Discriminant Analysis 3.1 Classical linear discriminatio 3.2 Linear discriminants via regression 3.3 Robustness 3.4 Shrinkage methods 3.5 Logistic discrimination 3.6 Linear separatio andperceptrons4 Flexible Diseriminants 4.1 Fitting smooth parametric functions 4.2 Radial basis functions 4.3 Regularization5 Feed-forward Neural Networks 5.1 Biological motivation 5.2 Theory 5.3 Learning algorithms 5.4 Examples 5.5 Bayesian perspectives 5.6 Network complexity 5.7 Approximation results6 Non-parametric Methods 6.1 Non-parametric estlmation of class densities 6.2 Nearest neighbour methods 6 3 Learning vector quantization 6.4 Mixture representations7 Tree-structured Classifiers 7.1 Splitting rules 7.2 Pruning rules 7.3 Missing values 7.4 Earlier approaches 7.5 Refinements 7.6 Relationships to neural networks 7.7 Bayesian trees8 Belief Networks 8.1 Graphical models and networks 8.2 Causal networks 8 3 Learning the network structure 8.4 Boltzmann machines 8.5 Hierarchical mixtures of experts9 Unsupervised Methods 9.1 Projection methods 9.2 Multidimensional scaling 9.3 Clustering algorithms 9.4 Self-organizing maps10 Finding Good Pattern Features 10.1 Bounds for the Bayes error 10.2 Normal class distributions 10.3 Branch-and-bound techniques 10.4 Feature extractionA Statistical Sidelines A.1 Maximum likelihood and MAP estimation A.2 The EM algorithm A.3 Markov chain Monte Carlo A.4 Axioms for conditional independence A.5 OptimizationGlossaryReferencesAuthor IndexSubject Index