An edition of Neural networks: a comprehensive foundation
(1994)
Neural networks
a comprehensive foundation
2nd ed.
by Simon S. Haykin
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This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
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| Edition | Availability |
|---|---|
1
Neural networks: a comprehensive foundation
1999, Prentice Hall
in English
- 2nd ed.
0132733501 9780132733502
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2
Neural networks: a comprehensive foundation
1994, Macmillan, Maxwell Macmillan Canada, Maxwell Macmillan International
in English
0023527617 9780023527616
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Book Details
Table of Contents
Preface
Acknowledgments
Abbreviations 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 Pruning Techniques
4.16. Virtues and Limitations of Back-Propagation Learning
4.17. Accelerated Convergence of Back-Propagation Learning
4.18. Supervised Learning Viewed as an Optimization Problem
4.19. Convolutional Networks
4.20. Summary and Discussion
Notes and References
Problems
5. Radial-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. Principal Components Analysis
8.4. Hebbian-Based Maximum Eigenfilter
8.5. Hebbian-Based Principal Components Analysis
8.6. Computer Experiment: Image Coding
8.7. Adaptive Principal 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. Learning Vector Quantization
9.8. Computer Experiment: Adaptive Pattern Classification
9.9. Hierarchical Vector Quantization
9.10. Contextual Maps
9.11. Summary and Discussion
Notes and References
Problems
10. Information-Theoretic Models
10.1. Introduction
10.2. Entropy
10.3. Maximum Entropy Principle
10.4. Mutual Information
10.5. Kullback-Leibler Divergence
10.6. Mutual Information as an Objective Function to Be Optimized
10.7. Maximum Mutual Information Principle
10.8. Infomax and Redundancy Reduction
10.9. Spatially Coherent Features
10.10. Spatially Incoherent Features
10.11. Independent Components Analysis
10.12. Computer Experiment
10.13. Maximum Likelihood Estimation
10.14. Maximum Entropy Method
10.15. Summary and Discussion
Notes and References
Problems
11. Stochastic Machines and Their Approximates Rooted in Statistical Mechanics
11.1. Introduction
11.2. Statistical Mechanics
11.3. Markov Chains
11.4. Metropolis Algorithm
11.5. Simulated Annealing
11.6. Gibbs Sampling
11.7. Boltzmann Machine
11.8. Sigmoid Belief Networks
11.9. Helmholtz Machine
11.10. Mean-Field Theory
11.11. Deterministic Boltzmann Machine
11.12. Deterministic Sigmoid Belief Networks
11.13. Deterministic Annealing
11.14. Summary and Discussion
Notes and References
Problems
12. Neurodynamic Programming
12.1. Introduction
12.2. Markovian Decision Processes
12.3. Bellman's Optimality Criterion
12.4. Policy Iteration
12.5. Value Iteration
12.6. Neurodynamic Programming
12.7. Approximate Policy Iteration
12.8. Q-Learning
12.9. Computer Experiment
12.10. Summary and Discussion
Notes and References
Problems
13. Temporal Processing Using Feedforward Networks
13.1. Introduction
13.2. Short-Term Memory Structures
13.3. Network Architectures for Temporal Processing
13.4. Focused Time Lagged Feedforward Networks
13.5. Computer Experiment
13.6. Universal Myopic Mapping Theorem
13.7. Spatio-Temporal Models of a Neuron
13.8. Distributed Time Lagged Feedforward Networks
13.9. Temporal Back-Propagation Algorithm
13.10. Summary and Discussion
Notes and References
Problems
14. Neurodynamics
14.1. Introduction
14.2. Dynamical Systems
14.3. Stability of Equilibrium States
14.4. Attractors
14.5. Neurodynamical Models
14.6. Manipulation of Attractors as a Recurrent Network Paradigm
14.7. Hopfield Models
14.8. Computer Experiment I
14.9. Cohen-Grossberg Theorem
14.10. Brain-State-in-a-Box Model
14.11. Computer Experiment II
14.12. Strange Attractors and Chaos
14.13. Dynamic Reconstruction of a Chaotic Process
14.14. Computer Experiment III
14.15. Summary and Discussion
Notes and References
Problems
15. Dynamically Driven Recurrent Networks
15.1. Introduction
15.2. Recurrent Network Architectures
15.3. State-Space Model
15.4. Nonlinear Autoregressive with Exogenous Inputs Model
15.5. Computational Power of Recurrent Networks
15.6. Learning Algorithms
15.7. Back-Propagation Through Time
15.8. Real-Time Recurrent Learning
15.9. Kalman Filters
15.10. Decoupled Extended Kalman Filters
15.11. Computer Experiment
15.12. Vanishing Gradients in Recurrent Networks
15.13. System Identification
15.14. Model-Reference Adaptive Control
15.15. Summary and Discussion
Notes and References
Problems
Epilogue
Bibliography
Index
Edition Notes
Includes bibliographical references (p. 796-836) and index.
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