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

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Formal Verification in Railways,ication system. In this method, the evidential C-means algorithm is first used to obtain credal partitions of the training set. The clustering process operates in a supervised way by means of weighted product-space clustering with the goals of obtaining both good inter-cluster separability and inner

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/183300.jpg

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https://doi.org/10.1007/978-3-642-68195-0including 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.

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India Studies in Business and Economicsusing Dempster’s rule of combination. From that perspective, the output probabilities from such classifiers can be seen as normalized plausibilities, for some mass functions that can be laid bare. This finding suggests that the theory of belief functions is a more general framework for classifier construction than is usually considered.