Bayesian Optimization and Data Science

Francesco Archetti

ISBN 10: 3030244938 / ISBN 13: 9783030244934

Published by Springer, Springer VS Okt 2019, 2019

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 140 pp. Englisch.

Seller Inventory # 9783030244934

Bibliographic Details

• Title: Bayesian Optimization and Data Science
• Publisher: Springer, Springer VS Okt 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 3030244938
• ISBN 13: 9783030244934
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 8D millimeters 235 millimeters width by 155 millimeters height by 8 millimeters depth
• Weight: 224 grams

About this title

Synopsis

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.