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Titlebook: Belief Functions: Theory and Applications; 8th International Co Yaxin Bi,Anne-Laure Jousselme,Thierry Denoeux Conference proceedings 2024 T

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發(fā)表于 2025-3-21 20:01:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Belief Functions: Theory and Applications
期刊簡(jiǎn)稱(chēng)8th International Co
影響因子2023Yaxin Bi,Anne-Laure Jousselme,Thierry Denoeux
視頻videohttp://file.papertrans.cn/193/192640/192640.mp4
學(xué)科分類(lèi)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Belief Functions: Theory and Applications; 8th International Co Yaxin Bi,Anne-Laure Jousselme,Thierry Denoeux Conference proceedings 2024 T
影響因子.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..
Pindex Conference proceedings 2024
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Conflict Management in?a?Distance to?Prototype-Based Evidential Deep LearningThe experimental results are obtained using a lidar-camera cross-fusion architecture with evidential formulation based on Dempster-Shafer’s theory. The model is investigated on a road detection task and it uses the KITTI dataset.
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https://doi.org/10.1007/978-3-540-44760-3kes place currently. For voluminous datasets, users can also adjust the update frequency according to their specific requirements. Compared to state-of-the-art (SoTA) stream clustering algorithms, IEC demonstrates better clustering accuracy and comparable runtime across four benchmark datasets. IEC
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1 Deformation behaviour of steel,pproach presented in the work [.]. Hence, this enables us to detect hallucinations by evaluating the conflict among evidence. Preliminary experiments were conducted on a state-of-the-art LVLM, mPLUG-Owl2. Results show that our approach exhibits an enhancement over baseline methods, particularly in c
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An Evidential Time-to-Event Prediction Model Based on?Gaussian Random Fuzzy Numbersdel is fit by minimizing a generalized negative log-likelihood function that accounts for both normal and censored data. Comparative experiments on two real-world datasets demonstrate the very good performance of our model as compared to the state-of-the-art.
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