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Scalable Uncertainty Management = 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Scalable Uncertainty Management/ edited by Nahla Ben Amor, Benjamin Quost, Martin Theobald.
Reminder of title:	13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings /
other author:	Ben Amor, Nahla.
Description:	XI, 452 p. 220 illus., 57 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-35514-2
ISBN:	9783030355142

Scalable Uncertainty Management = 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings /
Scalable Uncertainty Management13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings /[electronic resource] :edited by Nahla Ben Amor, Benjamin Quost, Martin Theobald. - 1st ed. 2019. - XI, 452 p. 220 illus., 57 illus. in color.online resource. - Lecture Notes in Artificial Intelligence ;11940. - Lecture Notes in Artificial Intelligence ;9285.

An Experimental Study on the Behaviour of Inconsistency Measures -- Inconsistency Measurement Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study -- The Hidden Elegance of Causal Interaction Models -- Computational Models for Cumulative Prospect Theory: Application to the Knapsack Problem Under Risk -- On a new evidential C-Means algorithm with instance-level constraints -- Hybrid Reasoning on a Bipolar Argumentation Framework -- Active Preference Elicitation by Bayesian Updating on Optimality Polyhedra -- Selecting Relevant Association Rules From Imperfect Data -- Evidential classification of incomplete data via imprecise relabelling: Application to plastic sorting -- An analogical interpolation method for enlarging a training dataset -- Towards a reconciliation between reasoning and learning - A position paper -- CP-nets, π-pref nets, and Pareto dominance -- Measuring Inconsistency through Subformula Forgetting Explaining Hierarchical Multi-Linear Models -- Assertional Removed Sets Merging of DL-Lite Knowledge Bases -- An Interactive Polyhedral Approach for Multi-Objective Combinatorial Optimization with Incomplete Preference Information -- Open-Mindedness of Gradual Argumentation Semantics -- Approximate Querying on Property Graphs -- Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants -- On cautiousness and expressiveness in interval-valued logic -- Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions -- Evidence Propagation and Consensus Formation in Noisy Environments -- Order-Independent Structure Learning of Multivariate Regression Chain Graphs -- l Comparison of analogy-based methods for predicting preferences -- Using Convolutional Neural Network in Cross-Domain Argumentation Mining Framework -- ConvNet and Dempster-Shafer Theory for Object Recognition -- On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions -- Efficient Mo ̈bius Transformations and their applications to D-S Theory -- From shallow to deep interactions between knowledge representation, reasoning and machine learning -- Dealing with Continuous Variables in Graphical Models -- Towards Scalable and Robust Sum-Product Networks -- Learning Models over Relational Data:A Brief Tutorial -- Subspace Clustering and Some Soft Variants -- Algebraic Approximations for Weighted Model Counting.

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.

ISBN: 9783030355142

Standard No.: 10.1007/978-3-030-35514-2doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Scalable Uncertainty Management = 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings /
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