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Scalable uncertainty management15th International Conference, SUM 2022, Paris, France, October 17-19, 2022 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scalable uncertainty managementedited by Florence Dupin de Saint-Cyr, Meltem Ozturk-Escoffier, Nico Potyka.
Reminder of title:	15th International Conference, SUM 2022, Paris, France, October 17-19, 2022 : proceedings /
remainder title:	SUM 2022
other author:	Dupin de Saint-Cyr, Florence.
corporate name:
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 371 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Uncertainty (Information theory)
Online resource:	https://doi.org/10.1007/978-3-031-18843-5
ISBN:	9783031188435$q(electronic bk.)

Scalable uncertainty management15th International Conference, SUM 2022, Paris, France, October 17-19, 2022 : proceedings /
Scalable uncertainty management15th International Conference, SUM 2022, Paris, France, October 17-19, 2022 : proceedings /[electronic resource] :SUM 2022edited by Florence Dupin de Saint-Cyr, Meltem Ozturk-Escoffier, Nico Potyka. - Cham :Springer International Publishing :2022. - xvii, 371 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,135620302-9743 ;. - Lecture notes in computer science ;4891..

Tutorial Articles -- Full Papers: Non-classical Reasoning -- Full Papers: Inconsistency -- Full Papers: Decision Making and Social Choice -- Full Papers: Learning -- Full Papers: Explanation -- Short Papers: Non-classical Reasoning -- Short Papers: Explanation.

This book constitutes the refereed proceedings of the 15th International Conference on Scalable Uncertainty Management, SUM 2022, which was held in Paris, France, in October 2022. The 19 full and 4 short papers presented in this volume were carefully reviewed and selected from 25 submissions. Besides that, the book also contains 3 abstracts of invited talks and 2 tutorial papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. The chapter "Defining and Enforcing Descriptive Accuracy in Explanations: the Case of Probabilistic Classifiers" is licensed under the terms of the Creative Commons Attribution 4.0 International License.

ISBN: 9783031188435$q(electronic bk.)

Standard No.: 10.1007/978-3-031-18843-5doiSubjects--Topical Terms:

206405
Uncertainty (Information theory)

LC Class. No.: Q375 / .S85 2022

Dewey Class. No.: 003.54

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