Titlebook: Belief Functions: Theory and Applications; 7th International Co Sylvie Le Hégarat-Mascle,Isabelle Bloch,Emanuel Al Conference proceedings 2

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書目名稱Belief Functions: Theory and Applications影響因子(影響力)




書目名稱Belief Functions: Theory and Applications影響因子(影響力)學科排名




書目名稱Belief Functions: Theory and Applications網絡公開度




書目名稱Belief Functions: Theory and Applications網絡公開度學科排名




書目名稱Belief Functions: Theory and Applications被引頻次




書目名稱Belief Functions: Theory and Applications被引頻次學科排名




書目名稱Belief Functions: Theory and Applications年度引用




書目名稱Belief Functions: Theory and Applications年度引用學科排名




書目名稱Belief Functions: Theory and Applications讀者反饋




書目名稱Belief Functions: Theory and Applications讀者反饋學科排名

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Themenmotivation und Gang der Untersuchung,at can be summarized by three numbers characterizing the most plausible predicted value, variability around this value, and epistemic uncertainty. Experiments with real datasets demonstrate the very good performance of the method as compared to state-of-the-art evidential and statistical learning algorithms.

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Themenmotivation und Gang der Untersuchung,assifier, which can be scaled to 48 nodes (2688 cores) at a cluster named the Texas Advanced Computing Center Frontera, with several other parallel K-NN based algorithms over 4 large datasets. Our method is able to achieve state-of-the-art scaling efficiency and accuracy on the large datasets having more than 10 million samples.

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Conference proceedings 2022 2022..The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well-understood connections to other frameworks such as probability, possibility, and imprecise probability theories. It has been applied in diverse areas such as machine lear

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An Evidential Neural Network Model for?Regression Based on?Random Fuzzy Numbersat can be summarized by three numbers characterizing the most plausible predicted value, variability around this value, and epistemic uncertainty. Experiments with real datasets demonstrate the very good performance of the method as compared to state-of-the-art evidential and statistical learning algorithms.

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0302-9743 ubmissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more..978-3-031-17800-9978-3-031-17801-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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