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Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compičgne, France, December 1618, 2019, Proceedings 2019 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 452 pages, height x width: 235x155 mm, weight: 706 g, 57 Illustrations, color; 163 Illustrations, black and white; XI, 452 p. 220 illus., 57 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 11940
  • Izdošanas datums: 21-Nov-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030355136
  • ISBN-13: 9783030355135
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Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compiègne, France, December 1618, 2019, Proceedings 2019 ed.Palielināt
  • Formāts: Paperback / softback, 452 pages, height x width: 235x155 mm, weight: 706 g, 57 Illustrations, color; 163 Illustrations, black and white; XI, 452 p. 220 illus., 57 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 11940
  • Izdošanas datums: 21-Nov-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030355136
  • ISBN-13: 9783030355135
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783030355135.html
Keywords:

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.

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,
-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.