Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

Author:   Hitoshi Iba (University of Tokyo, Japan) ,  Nikolay Nikolaev
Publisher:   Springer Us
ISBN:  

9781280613852


Pages:   316
Publication Date:   01 January 2006
Format:   Undefined
Availability:   In stock   Availability explained
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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods


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Overview

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 model identification process by which to discover models that generalize and predict well. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks. Adaptive Learning of Polynomial Networks is a vital reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and for advanced-level students of genetic programming. Readers will strengthen their skills in creating efficient model representations and learning operators that efficiently sample the search space, and in navigating the search process through the design of objective fitness functions.

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Author:   Hitoshi Iba (University of Tokyo, Japan) ,  Nikolay Nikolaev
Publisher:   Springer Us
Imprint:   Springer Us
ISBN:  

9781280613852


ISBN 10:   1280613858
Pages:   316
Publication Date:   01 January 2006
Audience:   General/trade ,  General
Format:   Undefined
Publisher's Status:   Active
Availability:   In stock   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

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