Adaptive learning of polynomial networks : genetic programming, backpropagation and Bayesian methods
著者
書誌事項
Adaptive learning of polynomial networks : genetic programming, backpropagation and Bayesian methods
（Genetic and evolutionary computation series）
Springer Science + Business Media, c2006
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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 book further facilitates the discovery of polynomial models for time-series prediction.
目次
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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