Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods / Edition 1

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods / Edition 1

by Nikolay Nikolaev, Hitoshi Iba
     
 

ISBN-10: 144194060X

ISBN-13: 9781441940605

Pub. Date: 02/11/2011

Publisher: Springer US

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

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

Product Details

ISBN-13:
9781441940605
Publisher:
Springer US
Publication date:
02/11/2011
Series:
Genetic and Evolutionary Computation Series
Edition description:
Softcover reprint of hardcover 1st ed. 2006
Pages:
316
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Inductive Genetic Programming.- Tree-Like PNN Representations.- Fitness Functions and Landscapes.- Search Navigation.- Backpropagation Techniques.- Temporal Backpropagation.- Bayesian Inference Techniques.- Statistical Model Diagnostics.- Time Series Modelling.- Conclusions.

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