Bayesian Optimization and Data Science
Authors: Archetti, Francesco, Candelieri, Antonio
Free Preview- 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
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- About this book
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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.
- Table of contents (7 chapters)
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Automated Machine Learning and Bayesian Optimization
Pages 1-18
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From Global Optimization to Optimal Learning
Pages 19-35
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The Surrogate Model
Pages 37-56
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The Acquisition Function
Pages 57-72
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Exotic Bayesian Optimization
Pages 73-96
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Table of contents (7 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Bayesian Optimization and Data Science
- Authors
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- Francesco Archetti
- Antonio Candelieri
- Series Title
- SpringerBriefs in Optimization
- Copyright
- 2019
- Publisher
- Springer International Publishing
- Copyright Holder
- The Author(s), under exclusive license to Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-24494-1
- DOI
- 10.1007/978-3-030-24494-1
- Softcover ISBN
- 978-3-030-24493-4
- Series ISSN
- 2190-8354
- Edition Number
- 1
- Number of Pages
- XIII, 126
- Number of Illustrations
- 13 b/w illustrations, 39 illustrations in colour
- Topics
