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Titlebook: Belief Functions: Theory and Applications; 5th International Co Sébastien Destercke,Thierry Denoeux,Arnaud Martin Conference proceedings 20

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樓主: 太平間
41#
發(fā)表于 2025-3-28 15:04:11 | 只看該作者
General Geometry of Belief Function Combination,including Yager’s, Dubois’, and disjunctive combination, starting from the case of binary frames of discernment. Believability measures for unnormalised belief functions are also considered. A research programme to complete this analysis is outlined.
42#
發(fā)表于 2025-3-28 19:44:48 | 只看該作者
43#
發(fā)表于 2025-3-28 23:35:37 | 只看該作者
44#
發(fā)表于 2025-3-29 04:34:54 | 只看該作者
45#
發(fā)表于 2025-3-29 07:52:57 | 只看該作者
46#
發(fā)表于 2025-3-29 13:10:46 | 只看該作者
,Study of Distributed Data Fusion Using Dempster’s Rule and Cautious Operator,unctions to model uncertainties has been proposed for smart cars network. Since the origin of data coming from other cars is unknown, this algorithm uses the idempotent cautious operator in order to prevent data incest. This operator has been proved to be efficient in the case of transient errors an
47#
發(fā)表于 2025-3-29 16:49:02 | 只看該作者
Uncertainty-Aware Parzen-Rosenblatt Classifier for Multiattribute Data,meworks proposed in this area, determining the basic probability assignment remains an open issue. To address this problem, this paper proposes a novel Dempster-Shafer scheme based on Parzen-Rosenblatt windowing for multi-attribute data classification. More explicitly, training data are used to cons
48#
發(fā)表于 2025-3-29 21:39:29 | 只看該作者
49#
發(fā)表于 2025-3-30 00:57:33 | 只看該作者
Evidential Independence Maximization on Twitter Network,y are independent in their choices and decisions. Independent users may attract other users and make them adopt their point of view. A user is qualified as independent when his/her point of view does not depend on others ideas. Thus, the behavior of such a user is independent from other behaviors. D
50#
發(fā)表于 2025-3-30 07:47:19 | 只看該作者
An Evidential ,-nearest Neighbors Combination Rule for Tree Species Recognition,.-nearest neighbors (.-NN) combination rule. The proposed rule is adapted to classification problems where we have a large number of classes with an intra-class variability and an inter-class similarity like the problem of tree species recognition. Finally, we compare the performance of the proposed
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