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| Preface | p. ix |
| Notation | p. xii |
| Introduction and Examples | p. 1 |
| How do neural methods differ? | p. 4 |
| The pattern recognition task | p. 5 |
| Overview of the remaining chapters | p. 9 |
| Examples | p. 10 |
| Literature | p. 15 |
| Statistical Decision Theory | p. 17 |
| Bayes rules for known distributions | p. 18 |
| Parametric models | p. 26 |
| Logistic discrimination | p. 43 |
| Predictive classification | p. 45 |
| Alternative estimation procedures | p. 55 |
| How complex a model do we need? | p. 59 |
| Performance assessment | p. 66 |
| Computational learning approaches | p. 77 |
| Linear Discriminant Analysis | p. 91 |
| Classical linear discrimination | p. 92 |
| Linear discriminants via regression | p. 101 |
| Robustness | p. 105 |
| Shrinkage methods | p. 106 |
| Logistic discrimination | p. 109 |
| Linear separation and perceptrons | p. 116 |
| Flexible Discriminants | p. 121 |
| Fitting smooth parametric functions | p. 122 |
| Radial basis functions | p. 131 |
| Regularization | p. 136 |
| Feed-forward Neural Networks | p. 143 |
| Biological motivation | p. 145 |
| Theory | p. 147 |
| Learning algorithms | p. 148 |
| Examples | p. 160 |
| Bayesian perspectives | p. 163 |
| Network complexity | p. 168 |
| Approximation results | p. 173 |
| Non-parametric Methods | p. 181 |
| Non-parametric estimation of class densities | p. 181 |
| Nearest neighbour methods | p. 191 |
| Learning vector quantization | p. 201 |
| Mixture representations | p. 207 |
| Tree-structured Classifiers | p. 213 |
| Splitting rules | p. 216 |
| Pruning rules | p. 221 |
| Missing values | p. 231 |
| Earlier approaches | p. 235 |
| Refinements | p. 237 |
| Relationships to neural networks | p. 240 |
| Bayesian trees | p. 241 |
| Belief Networks | p. 243 |
| Graphical models and networks | p. 246 |
| Causal networks | p. 262 |
| Learning the network structure | p. 275 |
| Boltzmann machines | p. 279 |
| Hierarchical mixtures of experts | p. 283 |
| Unsupervised Methods | p. 287 |
| Projection methods | p. 288 |
| Multidimensional scaling | p. 305 |
| Clustering algorithms | p. 311 |
| Self-organizing maps | p. 322 |
| Finding Good Pattern Features | p. 327 |
| Bounds for the Bayes error | p. 328 |
| Normal class distributions | p. 329 |
| Branch-and-bound techniques | p. 330 |
| Feature extraction | p. 331 |
| Statistical Sidelines | p. 333 |
| Maximum likelihood and MAP estimation | p. 333 |
| The EM algorithm | p. 334 |
| Markov chain Monte Carlo | p. 337 |
| Axioms for conditional independence | p. 339 |
| Optimization | p. 342 |
| Glossary | p. 347 |
| References | p. 355 |
| Author Index | p. 391 |
| Subject Index | p. 399 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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