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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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