《模式识别与神经网络》是2009年06月
人民邮电出版社出版的图书，作者是（英）里普利。本书讲述了模式识别所涉及的统计方法、神经网络和机器学习等分支。
内容简介
《模式识别与神经网络》是模式识别和
神经网络方面的名著。书的内容从介绍和例子开始，主要涵盖
统计决策理论、线性判别分析、弹性判别分析、
前馈神经网络、
非参数方法、树结构分类、信念网、无监管方法、探寻优良的模式特性等方面的内容。
作者简介
里普利（Ripley）著名的统计学家，牛津大学应用统计教授。他在空间统计学、模式识别领域作出了重要贡献，对S的开发以及S-PLUSUS和R的推广应用有着重要影响。20世纪90年代他出版了
人工神经网络方面的著作，影响很大，引导统计学者开始关注机器学习和数据挖掘。除本书外，他还著有Modern Applied Statistics with S和S Programming。
图书目录
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 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