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024	7		_a10.1007/978-3-030-35514-2 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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245	1	0	_aScalable Uncertainty Management _h[electronic resource] : _b13th International Conference, SUM 2019, Compiègne, France, December 16-18, 2019, Proceedings / _cedited by Nahla Ben Amor, Benjamin Quost, Martin Theobald.
250			_a1st ed. 2019.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019.
300			_aXI, 452 p. 220 illus., 57 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v11940
505	0		_aAn 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 measureof 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.
520			_aThis 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.
650		0	_aArtificial intelligence. _93407
650		0	_aComputer science. _99832
650		0	_aComputer science _xMathematics. _93866
650		0	_aMathematical statistics. _99597
650	1	4	_aArtificial Intelligence. _93407
650	2	4	_aComputer Science Logic and Foundations of Programming. _942203
650	2	4	_aProbability and Statistics in Computer Science. _931857
700	1		_aBen Amor, Nahla. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9111626
700	1		_aQuost, Benjamin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9111627
700	1		_aTheobald, Martin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9111628
710	2		_aSpringerLink (Online service) _9111629
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030355135
776	0	8	_iPrinted edition: _z9783030355159
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v11940 _9111630
856	4	0	_uhttps://doi.org/10.1007/978-3-030-35514-2
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