Sequence Generation Using Unwords
Statistical context models for sequence generation provide probabilities for each event in a sequence, conditioned on the context or history of the event in the sequence. A fundamentally different type of generative method inverts this view entirely, stat
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Communications in Computer and Information Science
1168
Machine Learning and Knowledge Discovery in Databases International Workshops of ECML PKDD 2019 Würzburg, Germany, September 16–20, 2019 Proceedings, Part II
Communications in Computer and Information Science
1168
Commenced Publication in 2007 Founding and Former Series Editors: Phoebe Chen, Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, Krishna M. Sivalingam, Dominik Ślęzak, Takashi Washio, Xiaokang Yang, and Junsong Yuan
Editorial Board Members Simone Diniz Junqueira Barbosa Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Igor Kotenko St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia Lizhu Zhou Tsinghua University, Beijing, China
More information about this series at http://www.springer.com/series/7899
Peggy Cellier Kurt Driessens (Eds.) •
Machine Learning and Knowledge Discovery in Databases International Workshops of ECML PKDD 2019 Würzburg, Germany, September 16–20, 2019 Proceedings, Part II
123
Editors Peggy Cellier Institut National des Sciences Appliquées Rennes, France
Kurt Driessens Maastricht University Maastricht, The Netherlands
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-3-030-43886-9 ISBN 978-3-030-43887-6 (eBook) https://doi.org/10.1007/978-3-030-43887-6 © 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
The European Conference on Machine Learning and Principles and Practice of Kno