Apprentissage de SVM sur Données Bruitées
Résumé
In this paper, we provide a simple example which shows that soft margin support vector machines (CSvm) are not tolerant to uniform classification noise. In order to cope with this limitation, we propose a noise-tolerant version of CSvm which is based on an objective function that makes use of an estimator of the noise-free slack margins. The nice properties of this estimator are supported by a theoretical analysis as well as numerical simulations carried out on a synthetic dataset.
Origine : Fichiers produits par l'(les) auteur(s)
Loading...