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Computer Science - Theoretical Computer Science | Adaptive Learning of Polynomial Networks - Genetic Programming, Backpropagation and Bayesian Methods

Adaptive Learning of Polynomial Networks

Genetic Programming, Backpropagation and Bayesian Methods

Nikolaev, Nikolay, Iba, Hitoshi

2006, XIV, 316 p.

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This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data.

The text emphasizes an organized model identification process by which to discover models that generalize and predict well. 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 book is an essential reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, as well for advanced-level students of genetic programming.

Readers will strengthen their skills in creating efficient model representations and learning operators that efficiently sample the search space, and in navigating the search process through the design of objective fitness functions.

Content Level » Research

Keywords » Bayesian inference - algorithms - artificial intelligence - genetic programming - intelligence - learning - machine learning - navigation - programming

Related subjects » Artificial Intelligence - Theoretical Computer Science

Table of contents / Sample pages 

Inductive Genetic Programming.- Tree-Like PNN Representations.- Fitness Functions and Landscapes.- Search Navigation.- Backpropagation Techniques.- Temporal Backpropagation.- Bayesian Inference Techniques.- Statistical Model Diagnostics.- Time Series Modelling.- Conclusions.

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