Applications in Statistical Computing From Music Data Analysis to In
This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other comput
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Nadja Bauer · Katja Ickstadt · Karsten Lübke · Gero Szepannek · Heike Trautmann · Maurizio Vichi Editors
Applications in Statistical Computing From Music Data Analysis to Industrial Quality Improvement
Studies in Classification, Data Analysis, and Knowledge Organization
Managing Editors
Editorial Board
Wolfgang Gaul, Karlsruhe, Germany Maurizio Vichi, Rome, Italy Claus Weihs, Dortmund, Germany
Daniel Baier, Bayreuth, Germany Frank Critchley, Milton Keynes, UK Reinhold Decker, Bielefeld, Germany Edwin Diday, Paris, France Michael Greenacre, Barcelona, Spain Carlo Natale Lauro, Naples, Italy Jacqueline Meulman, Leiden, The Netherlands Paola Monari, Bologna, Italy Shizuhiko Nishisato, Toronto, Canada Noboru Ohsumi, Tokyo, Japan Otto Opitz, Augsburg, Germany Gunter Ritter, Passau, Germany Martin Schader, Mannheim, Germany
More information about this series at http://www.springer.com/series/1564
Nadja Bauer Katja Ickstadt Karsten Lübke Gero Szepannek Heike Trautmann Maurizio Vichi •
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Editors
Applications in Statistical Computing From Music Data Analysis to Industrial Quality Improvement
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Editors Nadja Bauer Department of Computer Science Dortmund University of Applied Sciences and Arts Dortmund, Germany Karsten Lübke Institute for Empirical Research and Statistics FOM University of Applied Sciences Essen, Germany Heike Trautmann Department of Information Systems University of Münster Münster, Germany
Katja Ickstadt Faculty of Statistics TU Dortmund University Dortmund, Germany Gero Szepannek School of Business Studies HOST University of Applied Sciences Stralsund Stralsund, Germany Maurizio Vichi Department of Statistical Sciences Sapienza University of Rome Rome, Italy
ISSN 1431-8814 ISSN 2198-3321 (electronic) Studies in Classification, Data Analysis, and Knowledge Organization ISBN 978-3-030-25146-8 ISBN 978-3-030-25147-5 (eBook) https://doi.org/10.1007/978-3-030-25147-5 © 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 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 bee
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