Belief Functions: Theory and Applications
Springer International Publishing (Verlag)
978-3-031-67976-6 (ISBN)
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.
.- 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.
| Erscheinungsdatum | 20.08.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XIII, 294 p. 51 illus., 40 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | combination rules • computational frameworks • Continuous belief functions • Data and Information Fusion • Functions • Geometry and distance metrics • Independence and graphical models • Information Fusion • machine learning • Mathematical Foundations • measures of uncertainty and conflict • Random Fuzzy Sets • statistical inference and optimization |
| ISBN-10 | 3-031-67976-8 / 3031679768 |
| ISBN-13 | 978-3-031-67976-6 / 9783031679766 |
| Zustand | Neuware |
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