Belief Functions: Theory and Applications

Table of Contents

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   Book Overview
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   Chapter 1 A Distributional Approach for Soft Clustering Comparison and Evaluation
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   Chapter 2 Causal Transfer Evidential Clustering
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   Chapter 3 A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels
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   Chapter 4 Evidential Clustering by Competitive Agglomeration
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   Chapter 5 Imperfect Labels with Belief Functions for Active Learning
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   Chapter 6 An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
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   Chapter 7 Ordinal Classification Using Single-Model Evidential Extreme Learning Machine
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   Chapter 8 Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory
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    Chapter 9 Evidential Regression by Synthesizing Feature Selection and Parameters Learning
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    Chapter 10 Distributed EK-NN Classification
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    Chapter 11 On Improving a Group of Evidential Sources with Different Contextual Corrections
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    Chapter 12 Measure of Information Content of Basic Belief Assignments
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    Chapter 13 Belief Functions on Ordered Frames of Discernment
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    Chapter 14 On Modelling and Solving the Shortest Path Problem with Evidential Weights
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    Chapter 15 Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning
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    Chapter 16 Cluster Decomposition of the Body of Evidence
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    Chapter 17 Evidential Trustworthiness Estimation for Cooperative Perception
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    Chapter 18 An Intelligent System for Managing Uncertain Temporal Flood Events
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    Chapter 19 A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference
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    Chapter 20 On Conditional Belief Functions in the Dempster-Shafer Theory
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    Chapter 21 Valid Inferential Models Offer Performance and Probativeness Assurances
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    Chapter 22 A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases
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    Chapter 23 The Extension of Dempster’s Combination Rule Based on Generalized Credal Sets
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    Chapter 24 A Correspondence Between Credal Partitions and Fuzzy Orthopartitions
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    Chapter 25 Toward Updating Belief Functions over Belnap-Dunn Logic
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    Chapter 26 Real Bird Dataset with Imprecise and Uncertain Values
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    Chapter 27 Addressing Ambiguity in Randomized Reinsurance Contracts Using Belief Functions
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    Chapter 28 Evidential Filtering and Spatio-Temporal Gradient for Micro-movements Analysis in the Context of Bedsores Prevention
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    Chapter 29 Hybrid Artificial Immune Recognition System with Improved Belief Classification Process