Belief Functions: Theory and Applications

Table of Contents

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   Book Overview
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   Chapter 1 An Evidential Collaborative Filtering Approach Based on Items Contents Clustering
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   Chapter 2 The Belief Functions Theory for Sensors Localization in Indoor Wireless Networks
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   Chapter 3 On Evidential Clustering with Partial Supervision
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   Chapter 4 Exploiting Domain-Experts Knowledge Within an Evidential Process for Case Base Maintenance
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   Chapter 5 The Kantorovich Problem and Wasserstein Metric in the Theory of Belief Functions
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   Chapter 6 Generalised Max Entropy Classifiers
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   Chapter 7 General Geometry of Belief Function Combination
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   Chapter 8 Logistic Regression Revisited: Belief Function Analysis
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    Chapter 9 From Relations Between Sets to Relations Between Belief Functions
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    Chapter 10 Application of Belief Functions to Levee Assessment
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    Chapter 11 Prejudiced Information Fusion Using Belief Functions
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    Chapter 12 A Heuristic Approach for the Robust Flight Level Assignment Problem
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    Chapter 13 Study of Distributed Data Fusion Using Dempster’s Rule and Cautious Operator
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    Chapter 14 Uncertainty-Aware Parzen-Rosenblatt Classifier for Multiattribute Data
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    Chapter 15 Birnbaum’s Importance Measure Extended for Non-coherent Systems
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    Chapter 16 Evidential Independence Maximization on Twitter Network
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    Chapter 17 An Evidential k -nearest Neighbors Combination Rule for Tree Species Recognition
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    Chapter 18 A Compact Belief Rule-Based Classification System with Evidential Clustering
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    Chapter 19 A Decomposable Entropy of Belief Functions in the Dempster-Shafer Theory
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    Chapter 20 An Evidential K -Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization
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    Chapter 21 Measuring Market Performance with Stochastic Demand: Price of Anarchy and Price of Uncertainty
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    Chapter 22 On the Conflict Measures Agreed with the Combining Rules
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    Chapter 23 Linear Belief Functions for Data Analytics
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    Chapter 24 Outer Approximations of Coherent Lower Probabilities Using Belief Functions
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    Chapter 25 An Ordered Family of Consistency Measures of Belief Functions
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    Chapter 26 Active Evidential Calibration of Binary SVM Classifiers
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    Chapter 27 Decision Making: A Beliefs, Preferences and Constraints Model
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    Chapter 28 Belief and Plausibility Functions on the Space of Scalar Products and Applications
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    Chapter 29 E2CM: An Evolutionary Version of Evidential C-Means Clustering Algorithm
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    Chapter 30 Contrasting Two Laws of Large Numbers from Possibility Theory and Imprecise Probability
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    Chapter 31 Improved Performance of EK-NNClus by Selecting Appropriate Parameter
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    Chapter 32 An Empirical Study to Determine the Optimal k in Ek-NNclus Method
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    Chapter 33 Evidential Community Detection Based on Density Peaks