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Scalable Uncertainty Management

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Cover of 'Scalable Uncertainty Management'

Table of Contents

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    Book Overview
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    Chapter 1 An Experimental Study on the Behaviour of Inconsistency Measures
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    Chapter 2 Inconsistency Measurement
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    Chapter 3 Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study
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    Chapter 4 The Hidden Elegance of Causal Interaction Models
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    Chapter 5 Computational Models for Cumulative Prospect Theory: Application to the Knapsack Problem Under Risk
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    Chapter 6 On a New Evidential C-Means Algorithm with Instance-Level Constraints
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    Chapter 7 Hybrid Reasoning on a Bipolar Argumentation Framework
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    Chapter 8 Active Preference Elicitation by Bayesian Updating on Optimality Polyhedra
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    Chapter 9 Selecting Relevant Association Rules From Imperfect Data
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    Chapter 10 Evidential Classification of Incomplete Data via Imprecise Relabelling: Application to Plastic Sorting
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    Chapter 11 An Analogical Interpolation Method for Enlarging a Training Dataset
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    Chapter 12 Towards a Reconciliation Between Reasoning and Learning - A Position Paper
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    Chapter 13 CP-Nets, $$\pi $$-pref Nets, and Pareto Dominance
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    Chapter 14 Measuring Inconsistency Through Subformula Forgetting
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    Chapter 15 Explaining Hierarchical Multi-linear Models
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    Chapter 16 Assertional Removed Sets Merging of DL-Lite Knowledge Bases
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    Chapter 17 An Interactive Polyhedral Approach for Multi-objective Combinatorial Optimization with Incomplete Preference Information
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    Chapter 18 Open-Mindedness of Gradual Argumentation Semantics
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    Chapter 19 Approximate Querying on Property Graphs
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    Chapter 20 Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants
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    Chapter 21 On Cautiousness and Expressiveness in Interval-Valued Logic
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    Chapter 22 Preference Elicitation with Uncertainty: Extending Regret Based Methods with Belief Functions
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    Chapter 23 Evidence Propagation and Consensus Formation in Noisy Environments
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    Chapter 24 Order-Independent Structure Learning of Multivariate Regression Chain Graphs
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    Chapter 25 Comparison of Analogy-Based Methods for Predicting Preferences
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    Chapter 26 Using Convolutional Neural Network in Cross-Domain Argumentation Mining Framework
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    Chapter 27 ConvNet and Dempster-Shafer Theory for Object Recognition
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    Chapter 28 On Learning Evidential Contextual Corrections from Soft Labels Using a Measure of Discrepancy Between Contour Functions
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    Chapter 29 Efficient Möbius Transformations and Their Applications to D-S Theory
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    Chapter 30 Dealing with Continuous Variables in Graphical Models
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    Chapter 31 Towards Scalable and Robust Sum-Product Networks
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    Chapter 32 Learning Models over Relational Data: A Brief Tutorial
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    Chapter 33 Subspace Clustering and Some Soft Variants
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Title
Scalable Uncertainty Management
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-35514-2
ISBNs
978-3-03-035513-5, 978-3-03-035514-2
Editors

Nahla Ben Amor, Benjamin Quost, Martin Theobald

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