Belief Functions: Theory and Applications: 8th International Conference, BELIEF 2024, Belfast, UK, September 2–4, 2024, Proceedings: Lecture Notes in Computer Science, cartea 14909
Editat de Yaxin Bi, Anne-Laure Jousselme, Thierry Denoeuxen Limba Engleză Paperback – 12 sep 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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Specificații
ISBN-13: 9783031679766
ISBN-10: 3031679768
Ilustrații: XX, 267 p.
Dimensiuni: 155 x 235 mm
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Cham, Switzerland
ISBN-10: 3031679768
Ilustrații: XX, 267 p.
Dimensiuni: 155 x 235 mm
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Cham, Switzerland
Cuprins
.- 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.
.- 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.