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Book Categories |
| Acknowledgments | ||
| Foreword | ||
| Ch. 1 | Introduction | 1 |
| Ch. 2 | Preliminaries of Information Theory and Neural Networks | 7 |
| Ch. 3 | Linear Feature Extraction: Infomax Principle | 41 |
| Ch. 4 | Independent Component Analysis: General Formulation and Linear Case | 65 |
| Ch. 5 | Nonlinear Feature Extraction: Boolean Stochastic Networks | 109 |
| Ch. 6 | Nonlinear Feature Extraction: Deterministic Neural Networks | 135 |
| Ch. 7 | Supervised Learning and Statistical Estimation | 169 |
| Ch. 8 | Statistical Physics Theory of Supervised Learning and Generalization | 187 |
| Ch. 9 | Composite Networks | 219 |
| Ch. 10 | Information Theory Based Regularizing Methods | 225 |
| References | 243 | |
| Index | 259 |
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
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Add An Information-Theoretic Approach to Neural Computing, Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective prov, An Information-Theoretic Approach to Neural Computing to the inventory that you are selling on WonderClubX
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Add An Information-Theoretic Approach to Neural Computing, Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective prov, An Information-Theoretic Approach to Neural Computing to your collection on WonderClub |