Belief Functions: Theory and Applications

Table of Contents

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   Book Overview
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   Chapter 1 α -Junctions of Categorical Mass Functions
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   Chapter 2 Truthfulness in Contextual Information Correction
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   Chapter 3 The Choice of Generalized Dempster-Shafer Rules for Aggregating Belief Functions Based on Imprecision Indices
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   Chapter 4 General Schemes of Combining Rules and the Quality Characteristics of Combining
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   Chapter 5 An Optimal Unified Combination Rule
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   Chapter 6 Evidential Logistic Regression for Binary SVM Classifier Calibration
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   Chapter 7 The Evidence-Theoretic k -NN Rule for Rank-Ordered Data: Application to Predict an Individual’s Source of Loan
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   Chapter 8 Belief Hierarchical Clustering
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    Chapter 9 Logistic Regression of Soft Labeled Instances via the Evidential EM Algorithm
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    Chapter 10 Training and Evaluating Classifiers from Evidential Data: Application to E 2 M Decision Tree Pruning
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    Chapter 11 Reflections on DS/AHP: Lessons to Be Learnt
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    Chapter 12 Evidential Database: A New Generalization of Databases?
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    Chapter 13 Belief Approach for Social Networks
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    Chapter 14 Methods Handling Accident and Traffic Jam Information with Belief Functions in VANETs
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    Chapter 15 Designing a Belief Function-Based Accessibility Indicator to Improve Web Browsing for Disabled People
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    Chapter 16 Belief Reasoning Model for Mapping Public Participation in Transport Planning
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    Chapter 17 Some Notes on Canonical Decomposition and Separability of a Belief Function
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    Chapter 18 A Relational Representation of Belief Functions
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    Chapter 19 Modeling Qualitative Assessments under the Belief Function Framework
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    Chapter 20 A Study on Generalising Bayesian Inference to Evidential Reasoning
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    Chapter 21 Partial Ranking by Incomplete Pairwise Comparisons Using Preference Subsets
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    Chapter 22 Mathematical Theory of Evidence in Navigation
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    Chapter 23 Application of Belief Functions Theory to Non Destructive Testing of Industrial Pieces
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    Chapter 24 Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions
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    Chapter 25 Evidential Object Recognition Based on Information Gain Maximization
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    Chapter 26 Evidence-Based Modelling of Organizational Social Capital with Incomplete Data: An NCaRBS Analysis
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    Chapter 27 Outliers in Evidential C-Means: An Empirical Exploration Using Survey Data on Organizational Social Capital
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    Chapter 28 Causal Compositional Models in Valuation-Based Systems
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    Chapter 29 Merging Possibilistic Networks through a Disjunctive Mode
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    Chapter 30 On the Estimation of Mass Functions Using Self Organizing Maps
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    Chapter 31 Second-Order Belief Hidden Markov Models
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    Chapter 32 Learning Parameters in Directed Evidential Networks with Conditional Belief Functions
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    Chapter 33 Econometric Forecasting Using Linear Regression and Belief Functions
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    Chapter 34 Modelling and Fusion of Imperfect Implication Rules
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    Chapter 35 Conflict between Belief Functions: A New Measure Based on Their Non-conflicting Parts
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    Chapter 36 On Marginal Problem in Evidence Theory
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    Chapter 37 Multi-Sensor Fusion Using Evidential SLAM for Navigating a Probe through Deep Ice
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    Chapter 38 Belief Fusion of Predictions of Industries in China’s Stock Market
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    Chapter 39 An Evidential Fusion Rule for Ambient Intelligence for Activity Recognition
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    Chapter 40 Evidential Fusion for Sentiment Polarity Classification
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    Chapter 41 On the Quality of Optimal Assignment for Data Association
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    Chapter 42 Data Association for Object Enumeration Using Belief Function Theory
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    Chapter 43 A Novel Methodology for Target Classification Based on Dempster-Shafer Theory
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    Chapter 44 A New Parameterless Credal Method to Track-to-Track Assignment Problem
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    Chapter 45 Geometric Interpretations of Conflict: A Viewpoint
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    Chapter 46 Fast Computation of L p Norm-Based Specialization Distances between Bodies of Evidence
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    Chapter 47 New Distance Measures of Evidence Based on Belief Intervals
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    Chapter 48 Erratum to: A Novel Methodology for Target Classification Based on Dempster-Shafer Theory