Description |
xxx, 906 pages : illustrations (some color) ; 24 cm |
Contents |
Ch. 1. Rosenblatt's perceptron -- Ch. 2. Model building through regression -- Ch. 3. The least-mean-square algorithm -- Ch. 4. Multilayer perceptrons -- Ch. 5. Kernel methods and radial-basis function networks -- Ch. 6. Support vector machines -- Ch. 7. Regularization theory -- Ch. 8. Principal-components analysis -- Ch. 9. Self-organizing maps -- Ch. 10. Information-theoretic learning models -- Ch. 11. Stochastic methods rooted in statistical mechanics -- Ch. 12. Dynamic programming -- Ch. 13. Neurodynamics -- Ch. 14. Bayesian filtering for state estimation of dynamic systems -- Ch. 15. Dynamically driven recurrent networks |
Notes |
Rev. ed of: Neural networks. 2nd ed., c1999 |
Bibliography |
Includes bibliographical references (pages 847-887) and index |
Notes |
Also available online (Table of contents) |
Subject |
Neural networks (Computer science) -- Problems, exercises, etc.
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Neural networks (Computer science)
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Adaptive filters.
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Genre/Form |
Problems and exercises.
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Author |
Haykin, Simon S., 1931-
Neural networks
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LC no. |
2008034079 |
ISBN |
9780131471399 |
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0131471392 |
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