Belief functions: theory and applications 8th international conference, BELIEF 2024, Belfast, UK, September 2-4, 2024 : proceedings
.- 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 lik...
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| Sprache: | eng |
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Springer
2024
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| Schriftenreihe: | Lecture notes in computer science
14909 |
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| Online Zugang: | Cover |
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| Zusammenfassung: | .- 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 Bernstein-von 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. This book constitutes the refereed proceedings of the 8th International Conference on Belief Functions, BELIEF 2024, held in Belfast, UK, in September 2-4, 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xiii, 294 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031679766 978-3-031-67976-6 |