《模式识别与神经网络》电子书下载

模式识别与神经网络txt,chm,pdf,epub,mobi下载
作者:里普利
出版社: 人民邮电
出版年: 2009-6
页数: 403
定价: 69.00元
ISBN: 9787115210647

内容简介 · · · · · ·

《模式识别与神经网络(英文版)》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。

《模式识别与神经网络(英文版)》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。

作者简介 · · · · · ·

B.D.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。

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 Literature2 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 approaches3 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 andperceptrons4 Flexible Diseriminants 4.1 Fitting smooth parametric functions 4.2 Radial basis functions 4.3 Regularization5 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 results6 Non-parametric Methods 6.1 Non-parametric estlmation of class densities 6.2 Nearest neighbour methods 6 3 Learning vector quantization 6.4 Mixture representations7 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 trees8 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 experts9 Unsupervised Methods 9.1 Projection methods 9.2 Multidimensional scaling 9.3 Clustering algorithms 9.4 Self-organizing maps10 Finding Good Pattern Features 10.1 Bounds for the Bayes error 10.2 Normal class distributions 10.3 Branch-and-bound techniques 10.4 Feature extractionA Statistical Sidelines A.1 Maximum likelihood and MAP estimation A.2 The EM algorithm A.3 Markov chain Monte Carlo A.4 Axioms for conditional independence A.5 OptimizationGlossaryReferencesAuthor IndexSubject Index

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 Literature2 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 approaches3 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 andperceptrons4 Flexible Diseriminants 4.1 Fitting smooth parametric functions 4.2 Radial basis functions 4.3 Regularization5 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 results6 Non-parametric Methods 6.1 Non-parametric estlmation of class densities 6.2 Nearest neighbour methods 6 3 Learning vector quantization 6.4 Mixture representations7 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 trees8 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 experts9 Unsupervised Methods 9.1 Projection methods 9.2 Multidimensional scaling 9.3 Clustering algorithms 9.4 Self-organizing maps10 Finding Good Pattern Features 10.1 Bounds for the Bayes error 10.2 Normal class distributions 10.3 Branch-and-bound techniques 10.4 Feature extractionA Statistical Sidelines A.1 Maximum likelihood and MAP estimation A.2 The EM algorithm A.3 Markov chain Monte Carlo A.4 Axioms for conditional independence A.5 OptimizationGlossaryReferencesAuthor IndexSubject Index
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