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Bayesian Optimization and Data Science
Taschenbuch von Antonio Candelieri (u. a.)
Sprache: Englisch

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Beschreibung
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems.

The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems.

The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
Zusammenfassung

Gives readers an idea of the potential of the application of Bayesian Optimization to both traditional feels and emerging ones

Provides full and updated coverage of the areas of constrained Bayesian Optimization and Safe Bayesian Optimization

Covers software resources, allowing readers to make informed and educated choices among the different platforms available to set up Bayesian Optimization components in academic and industrial activities

Allows a full understanding of the basic algorithmic framework, including recent proposals about acquisition functions

Inhaltsverzeichnis
1. Automated Machine Learning and Bayesian Optimization.- 2. From Global Optimization to Optimal Learning.- 3. The Surrogate Model.- 4. The Acquisition Function.- 5. Exotic BO.- 6. Software Resources.- 7. Selected Applications.
Details
Erscheinungsjahr: 2019
Fachbereich: Allgemeines
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: SpringerBriefs in Optimization
Inhalt: xiii
126 S.
13 s/w Illustr.
39 farbige Illustr.
126 p. 52 illus.
39 illus. in color.
ISBN-13: 9783030244934
ISBN-10: 3030244938
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Candelieri, Antonio
Archetti, Francesco
Auflage: 1st ed. 2019
Hersteller: Springer International Publishing
Springer International Publishing AG
SpringerBriefs in Optimization
Maße: 235 x 155 x 8 mm
Von/Mit: Antonio Candelieri (u. a.)
Erscheinungsdatum: 07.10.2019
Gewicht: 0,224 kg
Artikel-ID: 116781427
Zusammenfassung

Gives readers an idea of the potential of the application of Bayesian Optimization to both traditional feels and emerging ones

Provides full and updated coverage of the areas of constrained Bayesian Optimization and Safe Bayesian Optimization

Covers software resources, allowing readers to make informed and educated choices among the different platforms available to set up Bayesian Optimization components in academic and industrial activities

Allows a full understanding of the basic algorithmic framework, including recent proposals about acquisition functions

Inhaltsverzeichnis
1. Automated Machine Learning and Bayesian Optimization.- 2. From Global Optimization to Optimal Learning.- 3. The Surrogate Model.- 4. The Acquisition Function.- 5. Exotic BO.- 6. Software Resources.- 7. Selected Applications.
Details
Erscheinungsjahr: 2019
Fachbereich: Allgemeines
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: SpringerBriefs in Optimization
Inhalt: xiii
126 S.
13 s/w Illustr.
39 farbige Illustr.
126 p. 52 illus.
39 illus. in color.
ISBN-13: 9783030244934
ISBN-10: 3030244938
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Candelieri, Antonio
Archetti, Francesco
Auflage: 1st ed. 2019
Hersteller: Springer International Publishing
Springer International Publishing AG
SpringerBriefs in Optimization
Maße: 235 x 155 x 8 mm
Von/Mit: Antonio Candelieri (u. a.)
Erscheinungsdatum: 07.10.2019
Gewicht: 0,224 kg
Artikel-ID: 116781427
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