Bayesian Optimization and Data Science
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
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Francesco Archetti Antonio Candelieri
Bayesian Optimization and Data Science
123
SpringerBriefs in Optimization Series Editors Sergiy Butenko, Texas A&M University Department of Industrial and Systems Engineering, College Station, TX, USA Mirjam Dür, Department of Mathematics, University of Trier, Trier, Germany Panos M. Pardalos, ISE Department, University of Florida, Gainesville, FL, USA János D. Pintér, Lehigh University and PCS Inc., Halifax, PA, USA Stephen M. Robinson, University of Wisconsin-Madison, Madison, WI, USA Tamás Terlaky, Lehigh University, Bethlehem, PA, USA My T. Thai, Gainesville, FL, USA
SpringerBriefs in Optimization showcases algorithmic and theoretical techniques, case studies, and applications within the broad-based field of optimization. Manuscripts related to the ever-growing applications of optimization in applied mathematics, engineering, medicine, economics, and other applied sciences are encouraged.
More information about this series at http://www.springer.com/series/8918
Francesco Archetti Antonio Candelieri •
Bayesian Optimization and Data Science
123
Francesco Archetti Department of Computer Science Systems and Communications University of Milano-Bicocca Milan, Italy
Antonio Candelieri Department of Computer Science Systems and Communications University of Milano-Bicocca Milan, Italy
ISSN 2190-8354 ISSN 2191-575X (electronic) SpringerBriefs in Optimization ISBN 978-3-030-24493-4 ISBN 978-3-030-24494-1 (eBook) https://doi.org/10.1007/978-3-030-24494-1 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
“Data culture is decision culture” it’s h
Data Loading...
