Effective Statistical Learning Methods for Actuaries III Neural Netw
Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance.
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Effective Statistical Learning Methods for Actuaries III Neural Networks and Extensions
Springer Actuarial Editors-in-Chief Hansjoerg Albrecher, University of Lausanne, Lausanne, Switzerland Michael Sherris, UNSW, Sydney, NSW, Australia Series Editors Daniel Bauer, University of Wisconsin-Madison, Madison, WI, USA Stéphane Loisel, ISFA, Université Lyon 1, Lyon, France Alexander J. McNeil, University of York, York, UK Antoon Pelsser, Maastricht University, Maastricht, The Netherlands Ermanno Pitacco, Università di Trieste, Trieste, Italy Gordon Willmot, University of Waterloo, Waterloo, ON, Canada Hailiang Yang, The University of Hong Kong, Hong Kong, Hong Kong
This is a series on actuarial topics in a broad and interdisciplinary sense, aimed at students, academics and practitioners in the fields of insurance and finance. Springer Actuarial informs timely on theoretical and practical aspects of topics like risk management, internal models, solvency, asset-liability management, market-consistent valuation, the actuarial control cycle, insurance and financial mathematics, and other related interdisciplinary areas. The series aims to serve as a primary scientific reference for education, research, development and model validation. The type of material considered for publication includes lecture notes, monographs and textbooks. All submissions will be peer-reviewed.
More information about this subseries at http://www.springer.com/series/15682
Michel Denuit • Donatien Hainaut • Julien Trufin
Effective Statistical Learning Methods for Actuaries III Neural Networks and Extensions
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Michel Denuit Université Catholique Louvain Louvain-la-Neuve, Belgium
Donatien Hainaut Université Catholique de Louvain Louvain-la-Neuve, France
Julien Trufin Université Libre de Bruxelles Brussels, Belgium
ISSN 2523-3262 ISSN 2523-3270 (electronic) Springer Actuarial ISSN 2523-3289 ISSN 2523-3297 (electronic) Springer Actuarial Lecture Notes ISBN 978-3-030-25826-9 ISBN 978-3-030-25827-6 (eBook) https://doi.org/10.1007/978-3-030-25827-6 Mathematics Subject Classification (2010): C1, C46, C22, C38, C45, C61, 62P05, 62-XX, 68-XX, 62M45 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved 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 tru
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