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Adaptive Learning of Polynomial Networks
Genetic Programming, Backpropagation and Bayesian Methods
Nikolay Nikolaev, Hitoshi Iba
206,95 €
+ 413 points
Format
Description
This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized identification process by which to discover models that generalize and predict well. The investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks. Here is an essential reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, as well for advanced-level students of genetic programming.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 316
- Langue:
- Anglais
- Collection :
Caractéristiques
- EAN:
- 9780387312392
- Date de parution :
- 03-05-06
- Format:
- Livre relié
- Format numérique:
- Genaaid
- Dimensions :
- 159 mm x 244 mm
- Poids :
- 698 g
