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Communication Dans Un Congrès Année : 2019

On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions

Résumé

In this paper, a proposition is made to learn the parameters of evidential contextual correction mechanisms from a learning set composed of soft labelled data, that is data where the true class of each object is only partially known. The method consists in optimizing a measure of discrepancy between the values of the corrected contour function and the ground truth also represented by a contour function. The advantages of this method are illustrated by tests on synthetic and real data.
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Dates et versions

hal-03217317 , version 1 (04-05-2021)

Identifiants

  • HAL Id : hal-03217317 , version 1

Citer

Siti Mutmainah, Samir Hachour, Frédéric Pichon, David Mercier. On learning evidential contextual corrections from soft labels using a measure of discrepancy between contour functions. 13th International Conference on Scalable Uncertainty Management, SUM 2019, Dec 2019, Compiègne, France. ⟨hal-03217317⟩

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