Adaptive Learning of Polynomial Networks

1. Front Matter

   Pages i-xiv

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2. Introduction

   Pages 1-24

3. Inductive Genetic Programming

   Pages 25-54

4. Tree-Like PNN Representations

   Pages 55-80

5. Fitness Functions and Landscapes

   Pages 81-110

6. Search Navigation

   Pages 111-146

7. Backpropagation Techniques

   Pages 147-180

8. Temporal Backpropagation

   Pages 181-208

9. Bayesian Inference Techniques

   Pages 209-240

10. Statistical Model Diagnostics

    Pages 241-272

11. Time Series Modelling

    Pages 273-290

12. Conclusions

    Pages 291-294

13. Back Matter

    Pages 295-316

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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.

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

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)

• University of London, London

  Nikolay Y. Nikolaev

• The University of Tokyo, Tokyo

  Hitoshi Iba

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