Enhancing Surrogate-Based Optimization Through Parallelization: 1099 - Couverture souple

Rehbach, Frederik

9783031306112: Enhancing Surrogate-Based Optimization Through Parallelization: 1099

Couverture souple

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Editeur : Springer-Verlag GmbH, 2024

Afficher les exemplaires de cette �dition comportant l?ISBN

0 D'occasion

8 Neuf

De EUR 153,73

D�tails de l'�dition

�diteur: Springer-Verlag GmbH
Date d'�dition: 2024
Langue: anglais
ISBN 10: 3031306112
ISBN 13: 9783031306112
Reliure: Tapa blanda
Nombre de pages: 128
Coordonn�es du fabricant: non disponible
Personne responsable: Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3
Heidelberg
69115
Allemagne

R�sultats de recherche pour Enhancing Surrogate-Based Optimization Through Parallelizati...

Image fournie par le vendeur

Enhancing Surrogate-Based Optimization Through Parallelization

Rehbach, Frederik

Edit� par Springer, Berlin|Springer Nature Switzerland|Springer, 2024

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Neuf Couverture souple

impression � la demande

Vendeur : moluna, Greven, Allemagne

�valuation du vendeur 4 sur 5 �toiles

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (S. N� de r�f. du vendeur 1689479571

Contacter le vendeur

Acheter neuf

EUR 153,73

EUR 48,99 shipping
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Enhancing Surrogate-Based Optimization Through Parallelization

Frederik Rehbach

Edit� par Springer Nature Switzerland, Springer International Publishing Mai 2024, 2024

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. 128 pp. Englisch. N� de r�f. du vendeur 9783031306112

Contacter le vendeur

Acheter neuf

EUR 181,89

EUR 23 shipping
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Enhancing Surrogate-Based Optimization Through Parallelization

Frederik Rehbach

Edit� par Springer, 2024

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Neuf Taschenbuch

Vendeur : preigu, Osnabr�ck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Contacter le vendeur

Acheter neuf

EUR 155,95

EUR 70 shipping
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 5 disponible(s)

Ajouter au panier

Image d'archives

Enhancing Surrogate-Based Optimization Through Parallelization

Rehbach, Frederik

Edit� par Springer, 2024

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Neuf Couverture souple

Vendeur : Books Puddle, New York, NY, Etats-Unis

�valuation du vendeur 4 sur 5 �toiles

Etat : New. 2023rd edition NO-PA16APR2015-KAP. N� de r�f. du vendeur 26401171477

Contacter le vendeur

Acheter neuf

EUR 225,61

EUR 3,40 shipping
Exp�dition nationale�: Etats-Unis

Quantit� disponible : 4 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Enhancing Surrogate-Based Optimization Through Parallelization

Frederik Rehbach

Edit� par Springer Nature Switzerland, Springer Nature Switzerland Mai 2024, 2024

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Neuware -This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 128 pp. Englisch. N� de r�f. du vendeur 9783031306112

Contacter le vendeur

Acheter neuf

EUR 181,89

EUR 60 shipping
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Enhancing Surrogate-Based Optimization Through Parallelization

Frederik Rehbach

Edit� par Springer Nature Switzerland, Springer Nature Switzerland, 2024

ISBN 10 : 3031306112 ISBN 13 : 9783031306112

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible.Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case.Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently. N� de r�f. du vendeur 9783031306112

Contacter le vendeur