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| List of Contributors | ||
| Acknowledgments | ||
| 1 | Introduction | 1 |
| Pt. I | Approximation Theory | 3 |
| 2 | There Exists a Neural Network That Does Not Make Avoidable Mistakes | 5 |
| 3 | Multilayer Feedforward Networks Are Universal Approximators | 12 |
| 4 | Universal Approximation Using Feedforward Networks with Non-sigmoid Hidden Layer Activation Functions | 29 |
| 5 | Approximating and Learning Unknown Mappings Using Multilayer Feedforward Networks with Bounded Weights | 41 |
| 6 | Universal Approximation of an Unknown Mapping and Its Derivatives | 55 |
| Pt. II | Learning and Statistics | 79 |
| 7 | Neural Network Learning and Statistics | 81 |
| 8 | Learning in Artificial Neural Networks: a Statistical Perspective | 90 |
| Pt. III | Learning Theory | 133 |
| 9 | Some Asymptotic Results for Learning in Single Hidden Layer Feedforward Networks | 135 |
| 10 | Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings | 160 |
| 11 | Nonparametric Estimation of Conditional Quantiles Using Neural Networks | 191 |
| 12 | On Learning the Derivatives of an Unknown Mapping with Multilayer Feedforward Networks | 206 |
| 13 | Consequences and Detection of Misspecified Nonlinear Regression Models | 224 |
| 14 | Maximum Likelihood Estimation of Misspecified Models | 259 |
| 15 | Some Results for Sieve Estimation with Dependent Observations | |
| Epilogue | 323 | |
| Additional References | 325 | |
| Index | 327 |
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| $99.99 | Digital |
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