Neural smithing supervised learning in feedforward artificial neural networks
"Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting be...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Mass. :
MIT Press
1999.
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| Colección: | EBSCO Academic eBook Collection Complete.
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| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b30787063*spi |
Tabla de Contenidos:
- 1. Introduction
- 2. Supervised learning
- 3. Single-layer networks
- 4. MLP representational capabilities
- 5. Back-propagation
- 6. Learning rate and momentum
- 7. Weight-initialization techniques
- 8. The error surface
- 9. Faster variations of back-propagation
- 10. Classical optimization techniques
- 11. Genetic algorithms and neural networks
- 12. Constructive methods
- 13. Pruning algorithms
- 14. Factors influencing generalization
- 15. Generalization prediction and assessment
- 16. Heuristics for improving generalization
- 17. Effects of training with noisy inputs.
