模式識別與神經(jīng)網(wǎng)絡(luò)（英文版）

1 Introduction and Examples1
1.1 How do neural methods differ?4
1.2 The patterm recognition task5
1.3 Overview of the remaining chapters9
1.4 Examples10
1.5 Literature15
2 Statistical Decision Theory17
2.1 Bayes rules for known distributions18
2.2 Parametric models26
2.3 Logistic discrimination43
2.4 Predictive classification45
2.5Alternative estimation procedures55
2.6 How complex a model do we need?59
2.7 Performance assessment66
2.8 Computational learning approaches77
3 Linear DiscriminantAnalysis91
3.1 Classical linear discriminatio92
3.2 Linear discriminants via regression101
3.3 Robustness105
3.4 Shrinkage methods106
3.5 Logistic discrimination109
3.6 Linear separatio andperceptrons116
4 Flexible Diseriminants121
4.1 Fitting smooth parametric functions122
4.2 Radial basis functions131
4.3 Regularization136
5 Feed-forward Neural Networks143
5.1 Biological motivation145
5.2 Theory147
5.3 Learning algorithms148
5.4 Examples160
5.5 Bayesian perspectives163
5.6 Network complexity168
5.7Approximation results173
6 Non-parametric Methods181
6.1 Non-parametric estlmation of class densities181
6.2 Nearest neighbour methods191
6 3 Learning vector quantization201
6.4 Mixture representations207
7 Tree-structured Classifiers213
7.1 Splitting rules216
7.2 Pruning rules221
7.3 Missing values231
7.4 Earlier approaches235
7.5 Refinements237
7.6 Relationships to neural networks240
7.7 Bayesian trees241
8 Belief Networks243
8.1 Graphical models and networks246
8.2 Causal networks262
8 3 Learning the network structure275
8.4 Boltzmann machines279
8.5 Hierarchical mixtures of experts283
9 Unsupervised Methods287
9.1 Projection methods288
9.2 Multidimensional scaling305
9.3 Clustering algorithms311
9.4 Self-organizing maps322
10 Finding Good Pattern Features327
10.1 Bounds for the Bayes error328
10.2 Normal class distributions329
10.3 Branch-and-bound techniques330
10.4 Feature extraction331
A Statistical Sidelines333
A.1 Maximum likelihood and MAP estimation333
A.2 TheEMalgorithm334
A.3 Markov chain Monte Carlo337
A.4Axioms for dconditional indcpcndence339
A.5 Oprimization342
Glossary347
References355
Author Index391
Subject Index399