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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Contributor(s): Nikolaev, Nikolay (Author), Iba, Hitoshi (Author)
ISBN: 0387312390     ISBN-13: 9780387312392
Publisher: Springer
OUR PRICE:   $161.49  
Product Type: Hardcover - Other Formats
Published: May 2006
Qty:
Annotation: Adaptive Learning of Polynomial Networks delivers theoretical and practical knowledge for the development of algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks.

The text emphasizes the model identification process and presents

  • a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approaches,

  • alternative probabilistic search algorithms that discover the model architecture and neural network training techniques to find accurate polynomial weights,

  • a means of discovering polynomial models for time-series prediction, and

  • an exploration of the areas of artificial intelligence, machine learning, evolutionary computation and neural networks, covering definitions of the basic inductive tasks, presenting basic approaches for addressing these tasks, introducing the fundamentals of genetic programming, reviewing the error derivatives for backpropagation training, and explaining the basics of Bayesian learning.

This volume is an essential reference for researchers and practitioners interested in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and will also appeal to postgraduate and advanced undergraduate students of genetic programming. Readers willstrengthen their skills in creating both efficient model representations and learning operators that efficiently sample the search space, navigating the search process through the design of objective fitness functions, and examining the search performance of the evolutionary system.
Additional Information
BISAC Categories:
- Computers | Computer Science
- Computers | Intelligence (ai) & Semantics
Dewey: 537.623
LCCN: 2006920797
Series: Genetic and Evolutionary Computation
Physical Information: 0.92" H x 6.26" W x 9.6" (1.54 lbs) 336 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
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.