TY  - BOOK
AU  - Bi,Yaxin
AU  - Jousselme,Anne-Laure
AU  - Denoeux,Thierry
ED  - SpringerLink (Online service)
TI  - Belief Functions: Theory and Applications: 8th International Conference, BELIEF 2024, Belfast, UK, September 2-4, 2024, Proceedings 
T2  - Lecture Notes in Artificial Intelligence,
SN  - 9783031679773
AV  - Q334-342 
U1  - 006.3 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Artificial intelligence
KW  - Probabilities
KW  - Artificial Intelligence
KW  - Probability Theory
N1  -  -- 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-67977-3
ER  -