模式识别与神经网络(英文版)
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图灵原版计算机科学系列

模式识别与神经网络(英文版)

Brian D.Ripley (作者)
终止销售
本书是模式识别和神经网络方面的名著,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。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。
本书可作为统计与理工科研究生课程的教材,对模式识别和神经网络领域的研究人员也是极有价值的参考书。

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出版信息

  • 书  名模式识别与神经网络(英文版)
  • 系列书名图灵原版计算机科学系列
  • 执行编辑关于本书的内容有任何问题,请联系 傅志红
  • 出版日期2009-08-15
  • 书  号978-7-115-21064-7
  • 定  价69.00 元
  • 页  数416
  • 开  本16开
  • 出版状态终止销售
  • 原书名Pattern Recognition and Neural Networks
  • 原书号978-0-521-71770-0

同系列书

目录

Preface ix
Notation ym
1 Introduction and Examples 1
1.1 How do neural methods differ? 4
1.2 The patterm recognition task 5
1.3 Overview of the remaining chapters 9
1.4 Examples 10
1.5 Literature 15
2 Statistical Decision Theory 17
2.1 Bayes rules for known distributions 18
2.2 Parametric models 26
2.3 Logistic discrimination 43
2.4 Predictive classification 45
2.5 Alternative estimation procedures 55
2.6 How complex a model do we need? 59
2.7 Performance assessment 66
2.8 Computational learning approaches 77
3 Linear Discriminant Analysis 91
3.1 Classical linear discriminatio 92
3.2 Linear discriminants via regression 101
3.3 Robustness 105
3.4 Shrinkage methods 106
3.5 Logistic discrimination 109
3.6 Linear separatio andperceptrons 116
4 Flexible Diseriminants 121
4.1 Fitting smooth parametric functions 122
4.2 Radial basis functions 131
4.3 Regularization 136
5 Feed-forward Neural Networks 143
5.1 Biological motivation 145
5.2 Theory 147
5.3 Learning algorithms 148
5.4 Examples 160
5.5 Bayesian perspectives 163
5.6 Network complexity 168
5.7 Approximation results 173
6 Non-parametric Methods 181
6.1 Non-parametric estlmat~on of class densities 181
6.2 Nearest neighbour methods 191
6 3 Learning vector quantization 201
6.4 Mixture representations 207
7 Tree-structured Classifiers 213
7.1 Splitting rules 216
7.2 Pruning rules 221
7.3 Missing values 231
7.4 Earlier approaches 235
7.5 Refinements 237
7.6 Relationships to neural networks 240
7.7 Bayesian trees 241
8 Belief Networks 243
8.1 Graphical models and networks 246
8.2 Causal networks 262
8 3 Learning the network structure 275
8.4 Boltzmann machines 279
8.5 Hierarchical mixtures of experts 283
9 Unsupervised Methods 287
9.1 Projection methods 288
9.2 Multidimensional scaling 305
9.3 Clustering algorithms 311
9.4 Self-organizing maps 322
10 Finding Good Pattern Features 327
10.1 Bounds for the Bayes error 328
10.2 Normal class distributions 329
10.3 Branch-and-bound techniques 330
10.4 Feature extraction 331
A Statistical Sidelines 333
A.1 Maximum likelihood and MAP estimation 333
A.2 TheEMalgorithm 334
A.3 Markov chain Monte Carlo 337
A.4 Axioms for dconditional indcpcndence 339
A.5 Oprimization 342
Glossary 347
References 355
Author Index 391
Subject Index 399
  • 为什么不翻译成中文版
    sunny11111111  发表于 2013-02-23 11:50:25
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  • 为什么终止销售?
    编码世界的游侠  发表于 2018-07-05 11:28:28
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    • 版权到期或者因销量不佳无法重印。

      傅志红  发表于 2018-07-05 11:41:43