Neural networks and learning machines
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Upper Saddle River, New Jersey :
Pearson
c2009
|
| Edition: | Third edition |
| Subjects: |
Table of Contents:
- 1. Rosenblatt's perceptron
- 2. Model building through regression
- 3. The least-mean-square algorithm
- 4. Multilayer perceptrons
- 5. Kernel methods and radial-basis function networks
- 6. Support vector machines
- 7. Regularization theory
- 8. Principal-components analysis
- 9. Self-organizing maps
- 10. Information-theoretic learning models
- 11. Stochastic methods rooted in statistical mechanics
- 12. Dynamic programming
- 13. Neurodynamics
- 14. Bayesian filtering for state estimation of dynamic systems
- 15. Dynamically driven recurrent networks