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Synopsis
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.
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- ÉditeurSpringer International Publishing AG
- Date d'édition2023
- ISBN 10 3031306082
- ISBN 13 9783031306082
- ReliureRelié
- Langueanglais
- Nombre de pages115
- Coordonnées du fabricantnon disponible
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Enhancing Surrogate-Based Optimization Through Parallelization
Edité par
Springer Nature Switzerland, 2023
ISBN 10 : 3031306082
ISBN 13 : 9783031306082
Ancien ou d'occasion
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Vendeur : Buchpark, Trebbin, Allemagne
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Etat : Hervorragend. Zustand: Hervorragend | Seiten: 128 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 41769024/1
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Enhancing Surrogate-Based Optimization Through Parallelization
Edité par
Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10 : 3031306082
ISBN 13 : 9783031306082
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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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 réf. du vendeur 834045565
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Enhancing Surrogate-Based Optimization Through Parallelization
Edité par
Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10 : 3031306082
ISBN 13 : 9783031306082
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. 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 réf. du vendeur 9783031306082
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Enhancing Surrogate-Based Optimization Through Parallelization
ISBN 10 : 3031306082
ISBN 13 : 9783031306082
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Buch. 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 réf. du vendeur 9783031306082
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Enhancing Surrogate-Based Optimization Through Parallelization
ISBN 10 : 3031306082
ISBN 13 : 9783031306082
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Buch. 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 réf. du vendeur 9783031306082
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Enhancing Surrogate-Based Optimization Through Parallelization (Studies in Computational Intelligence, 1099)
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Enhancing Surrogate-Based Optimization Through Parallelization (Volume 1099)
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. Print on Demand. N° de réf. du vendeur 401430786
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Enhancing Surrogate-Based Optimization Through Parallelization (Volume 1099)
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18396027607
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Enhancing Surrogate-based Optimization Through Parallelization
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Hardcover. Etat : Brand New. 125 pages. 9.25x6.10x0.51 inches. In Stock. N° de réf. du vendeur x-3031306082
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Enhancing Surrogate-Based Optimization Through Parallelization (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2023
ISBN 10 : 3031306082
ISBN 13 : 9783031306082
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783031306082
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