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
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references
SUMMARY OR ABSTRACT
Text of Note
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
TOPICAL NAME USED AS SUBJECT
Entry Element
Bayesian statistical decision theory
Entry Element
Data mining
Entry Element
Machine learning
a03
a05
a05
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA279
.
5
PERSONAL NAME - PRIMARY RESPONSIBILITY
Archetti, Francesco, 1946-
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Candelieri, Antonio
ORIGINATING SOURCE
Country
Iran
Agency
University of Tehran. Library of College of Science