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Communication Dans Un Congrès Année : 2017

Classification paramétrique multi-classes à croyance

Paul Honeine

Résumé

The aim of parametric classification is to predict the target class of a new sample, under the hypothesis of known fitted distribution. A major drawback of this approach is the uncertainty due to the imprecise modeling of the training samples. For this purpose, a belief functions framework is provided to take into account uncertainties. The proposed method investigates the belief functions theory to assign a confidence weight to each class for any new sample. This approach yields a confidence-weighted parametric classification method for multi-class problems. The performance of the proposed method is validated by experiments on real data for indoor localization and for facial image recognition.
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Dates et versions

hal-01965911 , version 1 (27-12-2018)

Identifiants

  • HAL Id : hal-01965911 , version 1

Citer

Daniel Alshamaa, Farah Mourad-Chehade, Paul Honeine. Classification paramétrique multi-classes à croyance. Actes du 26-ème Colloque GRETSI sur le Traitement du Signal et des Images, 2017, Juan-Les-Pins, France. ⟨hal-01965911⟩
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