Adaptive Learning of Polynomial Networks

Genetic Programming, Backpropagation and Bayesian Methods
Softcover Nachdruck of hardcover 1. Auflage 2006
ISBN: 978-1-4419-4060-5
Verlag: Springer US

Buch, Englisch, 316 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 505 g

Reihe: Genetic and Evolutionary Computation

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.

Zielgruppe

Research

Autoren/Hrsg.

Iba, Hitoshi

Nikolaev, Nikolay

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.