Adaptive Learning of Polynomial Networks
Genetic Programming, Backpropagation and Bayesian Methods
(Sprache: Englisch)
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...
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Klappentext zu „Adaptive Learning of Polynomial Networks “
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.
Inhaltsverzeichnis zu „Adaptive Learning of Polynomial Networks “
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.
Bibliographische Angaben
- Autoren: Nikolay Nikolaev , Hitoshi Iba
- 2006, 2006, 316 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 0387312390
- ISBN-13: 9780387312392
- Erscheinungsdatum: 03.05.2006
Sprache:
Englisch
Rezension zu „Adaptive Learning of Polynomial Networks “
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)
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