Cantitate/Preț
Produs
Total produse
Vezi coșul
Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings de Nahla Ben Amor

Scalable Uncertainty Management: 13th International Conference, SUM 2019, Compiègne, France, December 16–18, 2019, Proceedings: Lecture Notes in Computer Science, cartea 11940

Editat de Nahla Ben Amor, Benjamin Quost, Martin Theobald
en Limba Engleză Paperback – 21 noi 2019
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.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 32800 lei

Preț vechi: 41000 lei
-20% Nou

Puncte Express: 492
Preț estimativ în valută:
6277€ • 6622$ • 5231£

Carte tipărită la comandă

Livrare economică 02-16 ianuarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030355135
ISBN-10: 3030355136
Pagini: 400
Ilustrații: XI, 452 p. 220 illus., 57 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.65 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

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 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.