A Fast Algorithm to Find Best Matching Units in Self-Organizing Maps
Self-Organizing Maps (SOM) are well-known unsupervised neural networks able to perform vector quantization while mapping an underlying regular neighbourhood structure onto the codebook. They are used in a wide range of applications. As with most properly
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Igor Farkaš Paolo Masulli Stefan Wermter (Eds.)
Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks Bratislava, Slovakia, September 15–18, 2020 Proceedings, Part II
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7407
Igor Farkaš Paolo Masulli Stefan Wermter (Eds.) •
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Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks Bratislava, Slovakia, September 15–18, 2020 Proceedings, Part II
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Editors Igor Farkaš Department of Applied Informatics Comenius University in Bratislava Bratislava, Slovakia Stefan Wermter Department of Informatics University of Hamburg Hamburg, Germany
Paolo Masulli Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby, Denmark
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-61615-1 ISBN 978-3-030-61616-8 (eBook) https://doi.org/10.1007/978-3-030-61616-8 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 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 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
Research on artificial neural networks has progressed ov
