| 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.
|
|
Neural networks (Computer science)
|
|
Adaptive filters.
|
| Genre/Form |
Problems and exercises.
|
| Author |
Haykin, Simon S., 1931-
Neural networks
|
| LC no. |
2008034079 |
| ISBN |
9780131471399 |
|
0131471392 |
|
