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© 2006

Adaptive Learning of Polynomial Networks

Genetic Programming, Backpropagation and Bayesian Methods

Benefits

  • 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

Book

Part of the Genetic and Evolutionary Computation book series (GEVO)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Pages 1-24
  3. Pages 111-146
  4. Pages 181-208
  5. Pages 273-290
  6. Pages 291-294
  7. Back Matter
    Pages 295-316

About this book

Introduction

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.

Keywords

Bayesian inference algorithms artificial intelligence genetic programming intelligence learning machine learning navigation programming

Authors and affiliations

  1. 1.University of LondonLondon
  2. 2.The University of TokyoTokyo

Bibliographic information

  • Book Title Adaptive Learning of Polynomial Networks
  • Book Subtitle Genetic Programming, Backpropagation and Bayesian Methods
  • Authors Nikolay Nikolaev
    Hitoshi Iba
  • Series Title Genetic and Evolutionary Computation
  • DOI https://doi.org/10.1007/0-387-31240-4
  • Copyright Information Springer Science+Business Media, Inc. 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-0-387-31239-2
  • Softcover ISBN 978-1-4419-4060-5
  • eBook ISBN 978-0-387-31240-8
  • Edition Number 1
  • Number of Pages XIV, 316
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Theory of Computation
    Artificial Intelligence
    Artificial Intelligence
  • Buy this book on publisher's site
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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)