Bayesian Optimization and Data Science
Author:   Francesco Archetti ,  Antonio Candelieri
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
ISBN:  

9783030244934


Pages:   126
Publication Date:   07 October 2019
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Bayesian Optimization and Data Science


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Overview

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.

Full Product Details

Author:   Francesco Archetti ,  Antonio Candelieri
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Weight:   0.454kg
ISBN:  

9783030244934


ISBN 10:   3030244938
Pages:   126
Publication Date:   07 October 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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.

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