Editors:
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11940)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
Conference series link(s): SUM: International Conference on Scalable Uncertainty Management
Conference proceedings info: SUM 2019.
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Table of contents (33 papers)
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Front Matter
About this book
This book constitutes the refereed proceedings of the 13th International Conference on Scalable Uncertainty Management, SUM 2019, which was held in Compiègne, France, in December 2019.
The 25 full, 4 short, 4 tutorial, 2 invited keynote papers presented in this volume were carefully reviewed and selected from 44 submissions. The conference is dedicated to the management of large amounts of complex, uncertain, incomplete, or inconsistent information. New approaches have been developed on imprecise probabilities, fuzzy set theory, rough set theory, ordinal uncertainty representations, or even purely qualitative models.
Editors and Affiliations
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Institut Supérieur de Gestion de Tunis, Bouchoucha, Tunisia
Nahla Ben Amor
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University of Technology of Compiègne, Compiègne, France
Benjamin Quost
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University of Luxembourg, Esch-Sur-Alzette, Luxembourg
Martin Theobald
Bibliographic Information
Book Title: Scalable Uncertainty Management
Book Subtitle: 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings
Editors: Nahla Ben Amor, Benjamin Quost, Martin Theobald
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-35514-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-35513-5Published: 21 November 2019
eBook ISBN: 978-3-030-35514-2Published: 02 December 2019
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XI, 452
Number of Illustrations: 163 b/w illustrations, 57 illustrations in colour
Topics: Artificial Intelligence, Logics and Meanings of Programs, Probability and Statistics in Computer Science
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