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Communication dans un congrès

A comparison between a Bayesian approach and a method based on continuous belief functions for pattern recognition

Anthony Fiche 1, 2 Arnaud Martin 3 Jean-Christophe Cexus 4 Ali Khenchaf 5
3 CORDIAL - Human-machine spoken dialogue
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes, ENSSAT - École Nationale Supérieure des Sciences Appliquées et de Technologie
4 Lab-STICC_ENSTAB_CID_TOMS ; REMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192), STIC - Pôle STIC [Brest]
5 Lab-STICC_ENSTAB_MOM_PIM ; REMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192), STIC - Pôle STIC [Brest]
Abstract : The theory of belief functions in discrete domain has been employed with success for pattern recognition. However, the Bayesian approach performs well provided that once the probability density functions are well estimated. Recently, the theory of belief functions has been more and more developed to the continuous case. In this paper, we compare results obtained by a Bayesian approach and a method based on continuous belief functions to characterize seabed sediments. The probability density functions of each feature of seabed sediments are unimodal and estimated from a Gaussian model and compared with an a-stable model.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-00704765
Contributeur : Annick Billon-Coat <>
Soumis le : mercredi 6 juin 2012 - 11:23:48
Dernière modification le : mercredi 5 août 2020 - 03:43:19

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  • HAL Id : hal-00704765, version 1

Citation

Anthony Fiche, Arnaud Martin, Jean-Christophe Cexus, Ali Khenchaf. A comparison between a Bayesian approach and a method based on continuous belief functions for pattern recognition. Belief 2012, May 2012, Compiègne, France. ⟨hal-00704765⟩

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