## Pattern Recognition and Neural NetworksPattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader. |

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### Contents

Introduction and Examples | 1 |

11 How do neural methods differ? | 4 |

12 The pattern recognition task | 5 |

13 Overview of the remaining chapters | 9 |

14 Examples | 10 |

15 Literature | 15 |

Statistical Decision Theory | 17 |

21 Bayes rules for known distributions | 18 |

63 Learning vector quantization | 201 |

64 Mixture representations | 207 |

Treestructured Classifiers | 213 |

71 Splitting rules | 216 |

72 Pruning rules | 221 |

73 Missing values | 231 |

74 Earlier approaches | 235 |

75 Refinements | 237 |

22 Parametric models | 26 |

23 Logistic discrimination | 43 |

24 Predictive classification | 45 |

25 Alternative estimation procedures | 55 |

26 How complex a model do we need? | 59 |

27 Performance assessment | 66 |

28 Computational learning approaches | 77 |

Linear Discriminant Analysis | 91 |

31 Classical linear discrimination | 92 |

32 Linear discriminants via regression | 101 |

33 Robustness | 105 |

34 Shrinkage methods | 106 |

35 Logistic discrimination | 109 |

36 Linear separation and perceptrons | 116 |

Flexible Discriminants | 121 |

41 Fitting smooth parametric functions | 122 |

42 Radial basis functions | 131 |

43 Regularization | 136 |

Feedforward Neural Networks | 143 |

51 Biological motivation | 145 |

52 Theory | 147 |

53 Learning algorithms | 148 |

54 Examples | 160 |

55 Bayesian perspectives | 163 |

56 Network complexity | 168 |

57 Approximation results | 173 |

Nonparametric Methods | 181 |

62 Nearest neighbour methods | 191 |

76 Relationships to neural networks | 240 |

77 Bayesian trees | 241 |

Belief Networks | 243 |

81 Graphical models and networks | 246 |

82 Causal networks | 262 |

83 Learning the network structure | 275 |

84 Boltzmann machines | 279 |

85 Hierarchical mixtures of experts | 283 |

Unsupervised Methods | 287 |

91 Projection methods | 288 |

92 Multidimensional scaling | 305 |

93 Clustering algorithms | 311 |

94 Selforganizing maps | 322 |

Finding Good Pattern Features | 327 |

101 Bounds for the Bayes error | 328 |

102 Normal class distributions | 329 |

103 Branchandbound techniques | 330 |

104 Feature extraction | 331 |

Statistical Sidelines | 333 |

A2 The EM algorithm | 334 |

A3 Markov chain Monte Carlo | 337 |

A4 Axioms for conditional independence | 339 |

A5 Optimization | 342 |

Glossary | 347 |

References | 355 |

391 | |

399 | |

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algorithm applied approach approximation asymptotic average Bayes risk Bayes rule Bayesian binary bound Breiman choose class densities classifier clique clusters conditional independence consider convergence covariance matrix cross-validation Cushing's syndrome dataset density estimation deviance dimension dissimilarity distance error rate example Figure Gibbs sampler gives hidden layer hidden units IEEE Transactions inputs iterative Journal kernel Kohonen linear combination linear discriminant log-likelihood logistic Machine Learning Mahalanobis distance marginal Markov Markov property maximize maximum likelihood measure methods minimize mixture moral graph multivariate neighbour Neural Computation neural networks node non-linear optimal outliers parameters pattern recognition perceptron plug-in posterior probabilities predictive principal components prior problem procedure projection pursuit Proposition pruning quadratic random variables regression sample Section shows smoothing splines split Statistical subset Suppose test set theory training set tree update values variance VC dimension vertex vertices weight decay WinF zero