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Belief Functions: Theory and Applications: 8th International Conference, BELIEF 2024, Belfast, UK, September 24, 2024, Proceedings 2024 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 294 pages, kõrgus x laius: 235x155 mm, 40 Illustrations, color; 11 Illustrations, black and white; XIII, 294 p. 51 illus., 40 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14909
  • Ilmumisaeg: 20-Aug-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031679768
  • ISBN-13: 9783031679766
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  • Formaat: Paperback / softback, 294 pages, kõrgus x laius: 235x155 mm, 40 Illustrations, color; 11 Illustrations, black and white; XIII, 294 p. 51 illus., 40 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14909
  • Ilmumisaeg: 20-Aug-2024
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031679768
  • ISBN-13: 9783031679766
Teised raamatud teemal:
This book constitutes the refereed proceedings of the 8th International Conference on Belief Functions, BELIEF 2024, held in Belfast, UK, in September 24, 2024.





The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on Machine learning; Statistical inference; Information fusion and optimization; Measures of uncertainty, conflict and distances; Continuous belief functions, logics, computation.
.- Machine learning. 



.- Deep evidential clustering of images.



.- Incremental Belief-peaks Evidential Clustering.



.- Imprecise Deep Networks for Uncertain Image Classification.



.- Dempster-Shafer Credal Probabilistic Circuits.



.- Uncertainty quantification in regression neural networks using
likelihood-based belief functions.



.- An evidential time-to-event prediction model based on Gaussian random
fuzzy numbers.



.- Object Hallucination Detection in Large Vision Language Models via
Evidential Conflict.



.- Multi-oversampling with evidence fusion for imbalanced data
classification.



.- An Evidence-based Framework For Heterogeneous Electronic Health Records: A
Case Study In Mortality Prediction.



.- Conflict Management in a Distance to Prototype-Based Evidential Deep
Learning.



.- A Novel Privacy Preserving Framework for Training Dempster-Shafer
Theory-based Evidential Deep Neural Network.



.- Statistical inference. 



.- Large-sample theory for inferential models: A possibilistic Bernsteinvon
Mises theorem.



.- Variational approximations of possibilistic inferential models.



.- Decision theory via model-free generalized fiducial inference.



.- Which statistical hypotheses are afflicted with false confidence?.



.- Algebraic expression for the relative likelihood-based evidential
prediction of an ordinal variable.



.- Information fusion and optimization. 



.- Why Combining Belief Functions on Quantum Circuits?.



.- SHADED: Shapley Value-based Deceptive Evidence Detection in Belief
Functions.



.- A Novel Optimization-Based Combination Rule for Dempster-Shafer Theory.



.- Fusing independent inferential models in a black-box manner.



.- Optimization under Severe Uncertainty: a Generalized Minimax Regret
Approach for Problems with Linear Objectives.



.- Measures of uncertainty, conflict and distances. 



.- A mean distance between elements of same class for rich labels.



.- Threshold Functions and Operations in the Theory of Evidence.



.- Mutual Information and Kullback-Leibler Divergence in the Dempster-Shafer
Theory.



.- An OWA-based Distance Measure for Ordered Frames of Discernment.



.- Automated Hierarchical Conflict Reduction for Crowdsourced Annotation
Tasks using Belief Functions.



.- Continuous belief functions, logics, computation. 



.- Gamma Belief Functions.



.- Combination of Dependent Gaussian Random Fuzzy Numbers.



.- A 3-valued Logical Foundation for Evidential Reasoning.



.- Accelerated Dempster Shafer using Tensor Train Representation.