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Belief Functions: Theory and Applications

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Cover of 'Belief Functions: Theory and Applications'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
  48. Altmetric Badge
    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
Attention for Chapter 8: Belief Hierarchical Clustering
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Chapter title
Belief Hierarchical Clustering
Chapter number 8
Book title
Belief Functions: Theory and Applications
Published in
arXiv, September 2014
DOI 10.1007/978-3-319-11191-9_8
Book ISBNs
978-3-31-911190-2, 978-3-31-911191-9
Authors

Wiem Maalel, Kuang Zhou, Arnaud Martin, Zied Elouedi, Maalel, Wiem, Zhou, Kuang, Martin, Arnaud, Elouedi, Zied

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 33%
Professor 1 17%
Student > Ph. D. Student 1 17%
Student > Doctoral Student 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 5 83%
Unknown 1 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2015.
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#18,388,295
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