Neural Networks : A comprehensive Foundation /
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Libro |
| Lenguaje: | Indeterminado Inglés |
| Publicado: |
New Jersey :
Prentice Hall,
1994
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| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 003 | armpuni | ||
| 005 | 20160923153929.0 | ||
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| 040 | |a armpuni |c armpuni | ||
| 041 | |a en | ||
| 080 | |a 519.7 | ||
| 100 | 1 | |a Haykin, Simon | |
| 245 | 1 | 0 | |a Neural Networks : |b A comprehensive Foundation / |c Simon Haykin |
| 260 | |a New Jersey : |b Prentice Hall, |c 1994 | ||
| 300 | |a xvi, 696 p.: |b il.;, 25 cm. | ||
| 500 | |a Apéndices p. 617 | ||
| 500 | |a Incluye abreviaciones y símbolos | ||
| 500 | |a Problemas al finald de cada capítulo | ||
| 550 | |a What is a neural network?. Learning process. Correlation matrix memory. The perceptron. Least-Mean-Square algorithm. Multilayer perceptrons. Back-propagation and differentiation. Radial-Basis Function networks. Recurrent networks rooted in statistical physics. Self-Organizing systems I: Hebbian learning. Self-organizing systems II: Competitive learning. Self-organizing systems III: Information-theoretic models. Modular networks. Temporal processing. Neurodynamics. VLSI Iplementations of neural networks. Pseudoinverse matrix memory. A general tool for convergence. Analysis of stochastic. Approximation algorithms. Statical thermodynamics. Fokker-plank equation. | ||
| 650 | 7 | |a AUTOORGANIZACION |2 LEMB | |
| 650 | 7 | |a INTELIGENCIA ARTIFICIAL |2 LEMB | |
| 650 | 7 | |a NEURAL NETWARKS |2 LEMB | |
| 650 | 7 | |a REDES NEURONALES |2 LEMB | |
| 942 | |c LB |2 cdu | ||
| 945 | |a MDC |d 1999-09-14 | ||
| 999 | |c 4459 |d 5634 | ||