Handling uncertainties in SVM classification

Abstract : This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. The solution provided can be used for both decision and posterior probability estimation. Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances.
Type de document :
Communication dans un congrès
IEEE Workshop on Statistical Signal Processing, Jun 2011, Nice, France. 2011
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Contributeur : Rémi Flamary <>
Soumis le : jeudi 16 juin 2011 - 21:28:28
Dernière modification le : mardi 3 octobre 2017 - 14:52:06
Document(s) archivé(s) le : samedi 17 septembre 2011 - 02:27:07


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  • HAL Id : hal-00582789, version 2
  • ARXIV : 1106.3397



Emilie Niaf, Rémi Flamary, Carole Lartizien, Stéphane Canu. Handling uncertainties in SVM classification. IEEE Workshop on Statistical Signal Processing, Jun 2011, Nice, France. 2011. 〈hal-00582789v2〉



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