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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation) - Softcover
Book 1 of 8: Genetic and Evolutionary Computation
Synopsis
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.
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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.
Review
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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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
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Adaptive Learning of Polynomial Networks
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 332 pp. Englisch. Seller Inventory # 9781441940605
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a shift in focus from the standard linear models toward highly nonlinear models that can be inferred by contemporary learning approachesPresents alternative probabilistic search algorithms that discover the model architecture and neural net. Seller Inventory # 4174420
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Adaptive Learning of Polynomial Networks | Genetic Programming, Backpropagation and Bayesian Methods
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Taschenbuch. Condition: Neu. Adaptive Learning of Polynomial Networks | Genetic Programming, Backpropagation and Bayesian Methods | Nikolay Nikolaev (u. a.) | Taschenbuch | Genetic and Evolutionary Computation | xiv | Englisch | 2011 | Springer | EAN 9781441940605 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 106971135
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Adaptive Learning of Polynomial Networks
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 332 pp. Englisch. Seller Inventory # 9781441940605
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Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9781441940605