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| HAL : hal-00328125, version 1 |
| Fiche détaillée |  Récupérer au format |
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| Inter. Conf. on Machine Learning and Application, orlando : États-Unis d'Amérique (2006) |
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| Modeling hesitation and conflict : a belief-based approach for multi-class problems |
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Thomas Burger 1, 2Oya Aran 3 |
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| (12/2006) |
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| Support Vector Machine (SVM) is a powerful tool for binary classification. Numerous methods are known to fuse several binary SVMs into multi-class (MC) classifiers. These methods are efficient, but an accurate study of the misclassified items leads to notice two sources of mistakes: (1) the response of each classifier does not use the entire information from the SVM, and (2) the decision method does not use the entire information from the classifier responses. In this paper, we present a method which partially prevents these two losses of information by applying Belief Theories (BTs) to SVM fusion, while keeping the efficient aspect of the classical methods. |
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| 1 : | Laboratoire des images et des signaux (LIS) |
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CNRS : UMR5083 – Institut National Polytechnique de Grenoble - INPG – Université Joseph Fourier - Grenoble I |
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| 2 : | France Télécom Recherche & Développement (FT R&D) |
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France Télécom |
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| 3 : | Dep. of Computer Engineering (BUM) |
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Bogazici University |
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| Domaine | : | Informatique/Apprentissage |
| hal-00328125, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00328125 | |
| oai:hal.archives-ouvertes.fr:hal-00328125 | |
| Contributeur : Thomas Burger | |
| Soumis le : Jeudi 9 Octobre 2008, 17:18:48 | |
| Dernière modification le : Dimanche 19 Octobre 2008, 14:11:47 | |
