Russell D. Reed and Robert J. Marks II NEURAL SMITHING
Mar 1999 ISBN 0-262-18190-8 352 pp. 105 illus. $57/£39.50 (hardback)
|  | Preface
1. |  | Introduction
2. |  | Supervised Learning
3. |  | Single-Layer Networks
4. |  | MLP Representational Capabilities
5. |  | Back-Propagation
6. |  | Learning Rate and Momentum
7. |  | Weight-Initialization Techniques
8. |  | The Error Surface
9. |  | Faster Variations of Back-Propagation
10. |  | Classical Optimization Techniques
11. |  | Genetic Algorithms and Neural Networks
12. |  | Constructive Methods
13. |  | Pruning Algorithms
14. |  | Factors Influencing Generalization
15. |  | Generalization Prediction and Assessment
16. |  | Heuristics for Improving Generalization
17. |  | Effects of Training with Noisy Inputs
|  | Appendices
A |  | Linear Regression
B |  | Principal Components Analysis
C |  | Jitter Calculations
D |  | Sigmoid-like Nonlinear Functions
|  | References
|  | Index
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