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SpringerBriefs in Optimization

Bayesian Optimization and Data Science

Authors: Archetti, Francesco, Candelieri, Antonio

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  • 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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eBook $44.99
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  • ISBN 978-3-030-24494-1
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Softcover $59.99
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About this book

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)

Table of contents (7 chapters)

Buy this book

eBook $44.99
price for USA in USD
  • ISBN 978-3-030-24494-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $59.99
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Bayesian Optimization and Data Science
Authors
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

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