Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods - Couverture souple
Livre 1 sur 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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Autres éditions populaires du même titre
Résultats de recherche pour Adaptive Learning of Polynomial Networks: Genetic Programmin...
Image d'archives
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
Vendeur : thebookforest.com, San Rafael, CA, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010. N° de réf. du vendeur BAY_00_SH_030282
Acheter neuf
EUR 86,52
Expédition à EUR 4,31
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
Adaptive Learning of Polynomial Networks (eng)
impression à la demandeVendeur : Brook Bookstore On Demand, Napoli, NA, Italie
Évaluation du vendeur 4 sur 5 étoiles
Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur BQ1G140JFM
Acheter neuf
EUR 126,26
Expédition à EUR 6,80
Expédition depuis Italie vers Etats-Unis
Expédition depuis Italie vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9781441940605_new
Acheter neuf
EUR 136,90
Expédition à EUR 13,89
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Adaptive Learning of Polynomial Networks
Edité par
Springer US, Springer New York Feb 2011, 2011
ISBN 10 : 144194060X
ISBN 13 : 9781441940605
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9781441940605
Acheter neuf
EUR 160,49
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image d'archives
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Edité par
Springer-Verlag New York Inc., 2011
ISBN 10 : 144194060X
ISBN 13 : 9781441940605
Neuf
Paperback / softback
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. N° de réf. du vendeur C9781441940605
Acheter neuf
EUR 165,94
Expédition à EUR 18,23
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Adaptive Learning of Polynomial Networks
Edité par
Springer US, 2011
ISBN 10 : 144194060X
ISBN 13 : 9781441940605
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Kartoniert / Broschiert. Etat : 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. N° de réf. du vendeur 4174420
Acheter neuf
EUR 136,16
Expédition à EUR 48,99
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Adaptive Learning of Polynomial Networks | Genetic Programming, Backpropagation and Bayesian Methods
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Adaptive Learning of Polynomial Networks | Genetic Programming, Backpropagation and Bayesian Methods | Nikolay Nikolaev (u. a.) | Taschenbuch | xiv | Englisch | 2011 | Humana | EAN 9781441940605 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 106971135
Acheter neuf
EUR 141,20
Expédition à EUR 70
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 5 disponible(s)
Image fournie par le vendeur
Adaptive Learning of Polynomial Networks
Edité par
Springer US, Springer New York Feb 2011, 2011
ISBN 10 : 144194060X
ISBN 13 : 9781441940605
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 332 pp. Englisch. N° de réf. du vendeur 9781441940605
Acheter neuf
EUR 160,49
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
Edité par
Springer US, Springer New York, 2011
ISBN 10 : 144194060X
ISBN 13 : 9781441940605
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9781441940605
Acheter neuf
EUR 166,62
Expédition à EUR 62,53
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : Brand New. 316 pages. 9.00x6.00x0.75 inches. In Stock. N° de réf. du vendeur x-144194060X
Acheter neuf
EUR 235,96
Expédition à EUR 14,50
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 2 disponible(s)