模式識別與神經網路

《模式識別與神經網路》是模式識別和神經網路方面的名著。書的內容從介紹和例子開始，主要涵蓋統計決策理論、線性判別分析、彈性判別分析、前饋神經網路、非參數方法、樹結構分類、信念網、無監管方法、探尋優良的模式特性等方面的內容。

里普利（Ripley）著名的統計學家，牛津大學應用統計教授。他在空間統計學、模式識別領域作出了重要貢獻，對S的開發以及S-PLUSUS和R的推廣應用有著重要影響。20世紀90年代他出版了人工神經網路方面的著作，影響很大，引導統計學者開始關注機器學習和數據挖掘。除本書外，他還著有Modern Applied Statistics with S和S Programming。

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　Literature

2　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 approaches

3　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 andperceptrons

4.0　Flexible Diseriminants

4.1　Fitting smooth parametric functions

4.2　Radial basis functions

4.3　Regularization

5　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 results

6　Non-parametric Methods

6.1　Non-parametric estlmation of class densities

6.2　Nearest neighbour methods

6 3　Learning vector quantization

6.4　Mixture representations

7　Tree-structured Classifiers

7.1　Splitting rules

7.2　Pruning rules

7.3徠　Missing values

7.4　Earlier approaches

7.5　Refinements

7.6　Relationships to neural networks

7.7　Bayesian trees

8　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 experts

9　Unsupervised Methods

……

10　Finding Good Pattern Features

A　Statistical Sidelines

Glossary

References

Author Index

Subject Index