Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)

Nikolaev, Nikolay Y.

ISBN 10: 144194060X / ISBN 13: 9781441940605

Published by Springer, 2011

About this Item

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Bibliographic Details

• Title: Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
• Publisher: Springer
• Publication Date: 2011
• Language: English
• ISBN 10: 144194060X
• ISBN 13: 9781441940605
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

Review

From the reviews:

"This book describes induction of polynomial neural networks from data. ... This book may be used as a textbook for an advanced course on special topics of machine learning." (Jerzy W. Grzymala-Busse, Zentralblatt MATH, Vol. 1119 (21), 2007)