模式識別與神經(jīng)網(wǎng)絡(luò)（英文版）

定　價：￥69.00

作　者：	（英）里普利著
出版社：	人民郵電出版社
叢編項：	圖靈原版計算機科學(xué)系列
標(biāo)　簽：	運籌學(xué)

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ISBN：	9787115210647	出版時間：	2009-08-01	包裝：	平裝
開本：	16開	頁數(shù)：	403	字?jǐn)?shù)：

內(nèi)容簡介

　　《模式識別與神經(jīng)網(wǎng)絡(luò)（英文版）》是模式識別和神經(jīng)網(wǎng)絡(luò)方面的名著，講述了模式識別所涉及的統(tǒng)計方法、神經(jīng)網(wǎng)絡(luò)和機器學(xué)習(xí)等分支。書的內(nèi)容從介紹和例子開始，主要涵蓋統(tǒng)計決策理論、線性判別分析、彈性判別分析、前饋神經(jīng)網(wǎng)絡(luò)、非參數(shù)方法、樹結(jié)構(gòu)分類、信念網(wǎng)、無監(jiān)管方法、探尋優(yōu)良的模式特性等方面的內(nèi)容。《模式識別與神經(jīng)網(wǎng)絡(luò)（英文版）》可作為統(tǒng)計與理工科研究生課程的教材，對模式識別和神經(jīng)網(wǎng)絡(luò)領(lǐng)域的研究人員也是極有價值的參考書。

作者簡介

　　里普利（B.D.Ripley）著名的統(tǒng)計學(xué)家，牛津大學(xué)應(yīng)用統(tǒng)計教授。他在空間統(tǒng)計學(xué)、模式識別領(lǐng)域作出了重要貢獻，對S的開發(fā)以及S-PLUSUS和R的推廣應(yīng)用有著重要影響。20世紀(jì)90年代他出版了人工神經(jīng)網(wǎng)絡(luò)方面的著作，影響很大，引導(dǎo)統(tǒng)計學(xué)者開始關(guān)注機器學(xué)習(xí)和數(shù)據(jù)挖掘。除本書外，他還著有Modern Applied Statistics with S和S Programming。

圖書目錄

1 Introduction and Examples1
1.1 How do neural methods differ?4
1.2 The patterm recognition task5
1.3 Overview of the remaining chapters9
1.4 Examples10
1.5 Literature15
2 Statistical Decision Theory17
2.1 Bayes rules for known distributions18
2.2 Parametric models26
2.3 Logistic discrimination43
2.4 Predictive classification45
2.5Alternative estimation procedures55
2.6 How complex a model do we need?59
2.7 Performance assessment66
2.8 Computational learning approaches77
3 Linear DiscriminantAnalysis91
3.1 Classical linear discriminatio92
3.2 Linear discriminants via regression101
3.3 Robustness105
3.4 Shrinkage methods106
3.5 Logistic discrimination109
3.6 Linear separatio andperceptrons116
4 Flexible Diseriminants121
4.1 Fitting smooth parametric functions122
4.2 Radial basis functions131
4.3 Regularization136
5 Feed-forward Neural Networks143
5.1 Biological motivation145
5.2 Theory147
5.3 Learning algorithms148
5.4 Examples160
5.5 Bayesian perspectives163
5.6 Network complexity168
5.7Approximation results173
6 Non-parametric Methods181
6.1 Non-parametric estlmation of class densities181
6.2 Nearest neighbour methods191
6 3 Learning vector quantization201
6.4 Mixture representations207
7 Tree-structured Classifiers213
7.1 Splitting rules216
7.2 Pruning rules221
7.3 Missing values231
7.4 Earlier approaches235
7.5 Refinements237
7.6 Relationships to neural networks240
7.7 Bayesian trees241
8 Belief Networks243
8.1 Graphical models and networks246
8.2 Causal networks262
8 3 Learning the network structure275
8.4 Boltzmann machines279
8.5 Hierarchical mixtures of experts283
9 Unsupervised Methods287
9.1 Projection methods288
9.2 Multidimensional scaling305
9.3 Clustering algorithms311
9.4 Self-organizing maps322
10 Finding Good Pattern Features327
10.1 Bounds for the Bayes error328
10.2 Normal class distributions329
10.3 Branch-and-bound techniques330
10.4 Feature extraction331
A Statistical Sidelines333
A.1 Maximum likelihood and MAP estimation333
A.2 TheEMalgorithm334
A.3 Markov chain Monte Carlo337
A.4Axioms for dconditional indcpcndence339
A.5 Oprimization342
Glossary347
References355
Author Index391
Subject Index399