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Bayesian Optimization and Data Science / / by Francesco Archetti, Antonio Candelieri



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Autore: Archetti Francesco Visualizza persona
Titolo: Bayesian Optimization and Data Science / / by Francesco Archetti, Antonio Candelieri Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Edizione: 1st ed. 2019.
Descrizione fisica: 1 online resource (XIII, 126 p. 52 illus., 39 illus. in color.)
Disciplina: 519.6
Soggetto topico: Operations research
Management science
Machine learning
Computer software
Statistics 
Operations Research, Management Science
Machine Learning
Mathematical Software
Bayesian Inference
Persona (resp. second.): CandelieriAntonio
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Bayesian Optimization and Data Science  Visualizza cluster
ISBN: 3-030-24494-6
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910349319503321
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Serie: SpringerBriefs in Optimization, . 2190-8354