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Genetic and Evolutionary Computation

Adaptive Learning of Polynomial Networks

Genetic Programming, Backpropagation and Bayesian Methods

Authors: Nikolaev, Nikolay, Iba, Hitoshi

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eBook $139.00
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  • ISBN 978-0-387-31240-8
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Hardcover $179.00
price for USA
  • ISBN 978-0-387-31239-2
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  • Usually dispatched within 3 to 5 business days.
Softcover $179.00
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  • ISBN 978-1-4419-4060-5
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  • Usually dispatched within 3 to 5 business days.
About this book

This book provides theoretical and practical knowledge for develop­ ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod­ els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib­ ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well (that is, predict well). The book off'ers statisticians a shift in focus from the standard f- ear models toward highly nonlinear models that can be found by con­ temporary learning approaches. Speciafists in statistical learning will read about alternative probabilistic search algorithms that discover the model architecture, and neural network training techniques that identify accurate polynomial weights. They wfil be pleased to find out that the discovered models can be easily interpreted, and these models assume statistical diagnosis by standard statistical means. Covering the three fields of: evolutionary computation, neural net­ works and Bayesian inference, orients the book to a large audience of researchers and practitioners.

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)


Table of contents (3 chapters)

Buy this book

eBook $139.00
price for USA
  • ISBN 978-0-387-31240-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $179.00
price for USA
  • ISBN 978-0-387-31239-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $179.00
price for USA
  • ISBN 978-1-4419-4060-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Adaptive Learning of Polynomial Networks
Book Subtitle
Genetic Programming, Backpropagation and Bayesian Methods
Authors
Series Title
Genetic and Evolutionary Computation
Copyright
2006
Publisher
Springer US
Copyright Holder
Springer-Verlag US
eBook ISBN
978-0-387-31240-8
DOI
10.1007/0-387-31240-4
Hardcover ISBN
978-0-387-31239-2
Softcover ISBN
978-1-4419-4060-5
Series ISSN
1932-0167
Edition Number
1
Number of Pages
XIV, 316
Topics