Bibliograph

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

Type

Label

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

Language

eng

resource.imageBitDepth

Literary Form

non fiction

Main title

Bayesian Optimization and Data Science

Medium

electronic resource

Nature of contents

dictionaries

Oclc number

1121267395

Responsibility statement

by Francesco Archetti, Antonio Candelieri

Series statement

SpringerBriefs in Optimization,, 2190-8354Springer eBooks.

Summary

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.--, Provided by publisher

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

Contributor