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

by Nikolay Nikolaev, Hitoshi Iba

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 book further facilitates the discovery of polynomial models for time-series prediction.

330 pages, Paperback

First published January 1, 2006