Overview
- Offers a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approaches
- Presents alternative probabilistic search algorithms that discover the model architecture and neural network training techniques to find accurate polynomial weights
- Facilitates the discovery of polynomial models for time-series prediction
- Includes supplementary material: sn.pub/extras
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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Table of contents (11 chapters)
Keywords
About this book
Reviews
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)
Authors and Affiliations
Bibliographic Information
Book Title: Adaptive Learning of Polynomial Networks
Book Subtitle: Genetic Programming, Backpropagation and Bayesian Methods
Authors: Nikolay Y. Nikolaev, Hitoshi Iba
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/0-387-31240-4
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2006
Hardcover ISBN: 978-0-387-31239-2Published: 03 May 2006
Softcover ISBN: 978-1-4419-4060-5Published: 11 February 2011
eBook ISBN: 978-0-387-31240-8Published: 18 August 2006
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XIV, 316
Topics: Theory of Computation, Artificial Intelligence, Artificial Intelligence