Bayesian Optimization and Data Science by Francesco Archetti (English) Paperback

Description: Bayesian Optimization and Data Science by Francesco Archetti, Antonio Candelieri 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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 BOs 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. Back Cover 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 BOs 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. Table of Contents 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. Feature 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 Details ISBN3030244938 Author Antonio Candelieri Year 2019 ISBN-10 3030244938 ISBN-13 9783030244934 Pages 126 Publication Date 2019-10-07 Language English Format Paperback DOI 10.1007/978-3-030-24494-1 Edition 1st Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Illustrations 39 Illustrations, color; 13 Illustrations, black and white; XIII, 126 p. 52 illus., 39 illus. in color. Edited by Pau Herrero Birth 1927 Death 1973 Affiliation Massachusetts Institute of Technology Position journalist Qualifications S. J. Publisher Springer Nature Switzerland AG Edition Description 1st ed. 2019 Series SpringerBriefs in Optimization DEWEY 004 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:131022000;

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