內容簡介
模式識別與神經網路本書可作為統計與理工科研究生課程的教材，對模式識別和神經網路領域的研究人員也是極有價值的參考書。
作者簡介
里普利（Ripley）著名的統計學家，牛津大學套用統計教授。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的推廣套用有著重要影響。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。
圖書目錄
1Introduction 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
2Statistical 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
3Linear 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
5Feed-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
6Non-parametric Methods
6.1 Non-parametric estlmation of class densities
6.2 Nearest neighbour methods
6 3Learning vector quantization
6.4 Mixture representations
7Tree-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
8Belief Networks
8.1 Graphical models and networks
8.2 Causal networks
8 3Learning the network structure
8.4 Boltzmann machines
8.5 Hierarchical mixtures of experts
9Unsupervised Methods
……
10Finding Good Pattern Features
AStatistical Sidelines
Glossary
References
Author Index
Subject Index
