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Book Categories |
Preface | ||
1 | Introduction | 1 |
2 | Supervised Learning | 7 |
3 | Single-Layer Networks | 15 |
4 | MLP Representational Capabilities | 31 |
5 | Back-Propagation | 49 |
6 | Learning Rate and Momentum | 71 |
7 | Weight-Initialization Techniques | 97 |
8 | The Error Surface | 113 |
9 | Faster Variations of Back-Propagation | 135 |
10 | Classical Optimization Techniques | 155 |
11 | Genetic Algorithms and Neural Networks | 185 |
12 | Constructive Methods | 197 |
13 | Pruning Algorithms | 219 |
14 | Factors Influencing Generalization | 239 |
15 | Generalization Prediction and Assessment | 257 |
16 | Heuristics for Improving Generalization | 265 |
17 | Effects of Training with Noisy Inputs | 277 |
A | Linear Regression | 293 |
B | Principal Components Analysis | 299 |
C | Jitter Calculations | 311 |
D | Sigmoid-like Nonlinear Functions | 315 |
References | 319 | |
Index | 339 |
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