Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
Language: English
Published by Springer, 2011
- Softcover
- Used
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- Title
- Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
- Author
- Nikolaev, Nikolay, Iba, Hitoshi
- Publisher
- Springer
- Publication year
- 2011
- Condition
- Like New
- Book Type
- book
- Binding
- Paperback
- Language
- English
- ISBN 10
- 144194060X
- ISBN 13
- 9781441940605
- Series
- Book 1 of 8: Genetic and Evolutionary Computation
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 identification process by which to discover models that generalize and predict well. The investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks. Here 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.
"Synopsis" may belong to another edition of this title.
Product Description
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.
"About the title" may belong to another edition of this title.
Mispah books
Redhill, Surrey, United Kingdom
AbeBooks seller since April 15, 2021
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