Adaptive Learning of Polynomial Networks

Genetic Programming, Backpropagation and Bayesian Methods
2006
ISBN: 978-0-387-31239-2
Verlag: Springer US

Buch, Englisch, 316 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1430 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 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.

Zielgruppe

Research

Autoren/Hrsg.

Iba, Hitoshi

Nikolaev, Nikolay

Fachgebiete

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

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.