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神经网络的综合基础（第2版 英文影印版）
作者：Simon Haykin
出版社：Prentice Hall/Pearson
出版时间：2001-11-01
ISBN：9787302049364
定价：¥78.00
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内容简介
本书特色简介：神经网络，更准确地说应是人工神经网络，是植根于神经科学、数学、统计学、物理学、计算机科学以及工程等学科的一种技术，以其能够根据输入进行“学习”的特性广泛应用于诸如建模、时间序列分析、模式识别、信号处理、控制等领域。作者着眼于神经网络的多学科交叉特性，为读者提供了一本系统的、综合了最新研究成果的、指导性的教材。主要内容包括四部分：引论性的材料；有指导的学习机；无指导的学习机；非线性动力学系统。
作者简介
暂缺《神经网络的综合基础（第2版 英文影印版）》作者简介
目录
Preface
Acknowledgments
Abbrevidtions and Symbols
1 Introduction
1.1 What Is a Neural Network?
1.2 Human Brain
1.3 Models of a Neuron
1.4 Neural Networks Viewed as Directed Graphs
1.5 Feedback
1.6 Network Architectures
1.7 Knowledge Representation
1.8 Artificial Intelligence and Neural Networks
1.9 Historical Notes
Notes and References
Problems
2 Learning Processes
2.1 Introduction
2.2 Error-Correction Learning
2.3 Memory-Based Learning
2.4 Hebbian Learning
2.5 Competitive Learning
2.6 Boltzmann Learning
2.7 Credit Assignment Problem
2.8 Learning with a Teacher
2.9 Learning without a Teacher
2.10 Learning Tasks
2.11 Memory
2.12 Adaptation
2.13 Statistical Nature of the Learning Process
2.14 Statistical Learning Theory
2.15 Probably Approximately Correct Model of Learning
2.16 Summary and Discussion
Notes and References
Problems
3 Single Layer Perceptrons
3.1 Introduction
3.2 Adaptive Filtering Problem
3.3 Unconstrained Optimization Techniques
3.4 Linear Least-Squares Filters
3.5 Least-Mean-Square Algorithm
3.6 Learning Curves
3.7 Learning Rate Annealing Techniques
3.8 Perceptron
3.9 Perceptron Convergence Theorem
3.10 Relation Between the Perceptron and Bayes Classifier for a Gaussian Environment
3.11 Summary and Discussion
Notes and References
Problems
4 Multilayer Perceptrons
4.1 Introduction
4.2 Some Preliminaries
4.3 Back-Propagation Algorithm
4.4 Summary of the Back-Propagation Algorithm
4.5 XOR Problem
4.6 Heuristics for Making the Back-Propagation Algorithm Perform Better
4.7 Output Representation and Decision Rule
4.8 Computer Experiment
4.9 Feature Detection
4.10 Back-Propagation and Differentiation
4.11 Hessian Matrix
4.12 Generalization
4.13 Approximations of Functions
4.14 Cross-Validation
4.15 Network PruningTechniques
4.16 Virtues and Limitations of Back-Propagation Learning
4.17 Accelerated Convergence of Back-Propagation Learning
4.18 Supervised Learning Viewed as an Optindzation Problem
4.19 Convolutional Networks
4.20 Summary and Discussion
Notes and References
Problems
5 RadiaI-Basis Function Networks
5.1 Introduction
5.2 Cover's Theorem on the Separability of Patterns
5.3 Interpolation Problem
5.4 Supervised Learning as an Ill-Posed Hypersurface Reconstruction Problem
5.5 Regularization Theory
5.6 Regularization Networks
5.7 Generalized Radial-Basis Function Networks
5.8 XOR Problem (Revisited)
5.9 Estimation of the Regularization Parameter
5.10 Approximation Properties of RBF Networks
5.11 Comparison of RBF Networks and Multilayer Perceptrons
5.12 Kernel Regression and Its Relation to RBF Networks
5.13 Learning Strategies
5.14 Computer Experiment
5.15 Summary and Discussion
Notes and References
Problems
6 Support Vector Machines
6.1 Introduction
6.2 Optimal Hyperplane for Linearly Separable Patterns
6.3 Optimal Hyperplane for Nonseparable Patterns
6.4 How to Build a Support Vector Machine for Pattern Recognition
6.5 Example: XOR Problem (Revisited)
6.6 Computer Experiment
6.7 e-Insensitive Loss Function
6.8 Support Vector Machines for Nonlinear Regression
6.9 Summary and Discussion
Notes and References
Problems
7 Committee Machines
7.1 Introduction
7.2 Ensemble Averaging
7.3 Computer Experiment I
7.4 Boosting
7.5 Computer Experiment II
7.6 Associative Gaussian Mixture Model
7.7 Hierarchical Mixture of Experts Model
7.8 Model Selection Using a Standard Decision Tree
7.9 A Priori and a Posteriori Probabilities
7.10 Maximum Likelihood Estimation
7.11 Learning Strategies for the HME Model
7.12 EM Algorithm
7.13 Application of the EM Algorithm to the HME Model
7.14 Summary and Discussion
Notes and References
Problems
8 Principal Components Analysis
8.1 Introduction
8.2 Some Intuitive Principles of Self-Organization
8.3 Prinmpal Components Analysis
8.4 Hebbian-Based Maximum Eigenfilter
8.5 Hebbian-Based Principal Components Analysis
8.6 Computer Experiment: Image Coding
8.7 Adaptive Princinal Components Analysis Using Lateral Inhibition
8.8 Two Classes of PCA Algorithms
8.9 Batch and Adaptive Methods of Computation
8.10 Kernel-Based Principal Components Analysis
8.11 Summary And Discussion
Notes And References
Problems
9 Self-Organizing Maps
9.1 Introduction
9.2 Two Basic Feature-Mapping Models
9.3 Self-Organizing Map
9.4 Summary of the SOM Algorithm
9.5 Properties of the Feature Map
9.6 Computer Simulations
9.7 Leamng Vector Quantization
9.8 Computer Experiment: Adaptive Pattern Classification
Acknowledgments
Abbrevidtions and Symbols
1 Introduction
1.1 What Is a Neural Network?
1.2 Human Brain
1.3 Models of a Neuron
1.4 Neural Networks Viewed as Directed Graphs
1.5 Feedback
1.6 Network Architectures
1.7 Knowledge Representation
1.8 Artificial Intelligence and Neural Networks
1.9 Historical Notes
Notes and References
Problems
2 Learning Processes
2.1 Introduction
2.2 Error-Correction Learning
2.3 Memory-Based Learning
2.4 Hebbian Learning
2.5 Competitive Learning
2.6 Boltzmann Learning
2.7 Credit Assignment Problem
2.8 Learning with a Teacher
2.9 Learning without a Teacher
2.10 Learning Tasks
2.11 Memory
2.12 Adaptation
2.13 Statistical Nature of the Learning Process
2.14 Statistical Learning Theory
2.15 Probably Approximately Correct Model of Learning
2.16 Summary and Discussion
Notes and References
Problems
3 Single Layer Perceptrons
3.1 Introduction
3.2 Adaptive Filtering Problem
3.3 Unconstrained Optimization Techniques
3.4 Linear Least-Squares Filters
3.5 Least-Mean-Square Algorithm
3.6 Learning Curves
3.7 Learning Rate Annealing Techniques
3.8 Perceptron
3.9 Perceptron Convergence Theorem
3.10 Relation Between the Perceptron and Bayes Classifier for a Gaussian Environment
3.11 Summary and Discussion
Notes and References
Problems
4 Multilayer Perceptrons
4.1 Introduction
4.2 Some Preliminaries
4.3 Back-Propagation Algorithm
4.4 Summary of the Back-Propagation Algorithm
4.5 XOR Problem
4.6 Heuristics for Making the Back-Propagation Algorithm Perform Better
4.7 Output Representation and Decision Rule
4.8 Computer Experiment
4.9 Feature Detection
4.10 Back-Propagation and Differentiation
4.11 Hessian Matrix
4.12 Generalization
4.13 Approximations of Functions
4.14 Cross-Validation
4.15 Network PruningTechniques
4.16 Virtues and Limitations of Back-Propagation Learning
4.17 Accelerated Convergence of Back-Propagation Learning
4.18 Supervised Learning Viewed as an Optindzation Problem
4.19 Convolutional Networks
4.20 Summary and Discussion
Notes and References
Problems
5 RadiaI-Basis Function Networks
5.1 Introduction
5.2 Cover's Theorem on the Separability of Patterns
5.3 Interpolation Problem
5.4 Supervised Learning as an Ill-Posed Hypersurface Reconstruction Problem
5.5 Regularization Theory
5.6 Regularization Networks
5.7 Generalized Radial-Basis Function Networks
5.8 XOR Problem (Revisited)
5.9 Estimation of the Regularization Parameter
5.10 Approximation Properties of RBF Networks
5.11 Comparison of RBF Networks and Multilayer Perceptrons
5.12 Kernel Regression and Its Relation to RBF Networks
5.13 Learning Strategies
5.14 Computer Experiment
5.15 Summary and Discussion
Notes and References
Problems
6 Support Vector Machines
6.1 Introduction
6.2 Optimal Hyperplane for Linearly Separable Patterns
6.3 Optimal Hyperplane for Nonseparable Patterns
6.4 How to Build a Support Vector Machine for Pattern Recognition
6.5 Example: XOR Problem (Revisited)
6.6 Computer Experiment
6.7 e-Insensitive Loss Function
6.8 Support Vector Machines for Nonlinear Regression
6.9 Summary and Discussion
Notes and References
Problems
7 Committee Machines
7.1 Introduction
7.2 Ensemble Averaging
7.3 Computer Experiment I
7.4 Boosting
7.5 Computer Experiment II
7.6 Associative Gaussian Mixture Model
7.7 Hierarchical Mixture of Experts Model
7.8 Model Selection Using a Standard Decision Tree
7.9 A Priori and a Posteriori Probabilities
7.10 Maximum Likelihood Estimation
7.11 Learning Strategies for the HME Model
7.12 EM Algorithm
7.13 Application of the EM Algorithm to the HME Model
7.14 Summary and Discussion
Notes and References
Problems
8 Principal Components Analysis
8.1 Introduction
8.2 Some Intuitive Principles of Self-Organization
8.3 Prinmpal Components Analysis
8.4 Hebbian-Based Maximum Eigenfilter
8.5 Hebbian-Based Principal Components Analysis
8.6 Computer Experiment: Image Coding
8.7 Adaptive Princinal Components Analysis Using Lateral Inhibition
8.8 Two Classes of PCA Algorithms
8.9 Batch and Adaptive Methods of Computation
8.10 Kernel-Based Principal Components Analysis
8.11 Summary And Discussion
Notes And References
Problems
9 Self-Organizing Maps
9.1 Introduction
9.2 Two Basic Feature-Mapping Models
9.3 Self-Organizing Map
9.4 Summary of the SOM Algorithm
9.5 Properties of the Feature Map
9.6 Computer Simulations
9.7 Leamng Vector Quantization
9.8 Computer Experiment: Adaptive Pattern Classification
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