One-class SVM in multi-task learning - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

One-class SVM in multi-task learning

Résumé

Multi-Task Learning (MTL) has become an active research topic in recent years. While most machine learning methods focus on the learning of tasks independently, multi-task learning aims to improve the generalization performance by training multiple related tasks simultaneously. This paper presents a new approach to multi-task learning based on one-class Support Vector Machine (one-class SVM). In the proposed approach, we first make the assumption that the model parameter values of different tasks are close to a certain mean value. Then, a number of one-class SVMs, one for each task, are learned simultaneously. Our multi-task approach is easy to implement since it only requires a simple modification of the optimization problem in the single one-class SVM. Experimental results demonstrate the effectiveness of the proposed approach.
Fichier principal
Vignette du fichier
He_ESREL_11.pdf (2.18 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00593397 , version 1 (09-04-2014)

Identifiants

  • HAL Id : hal-00593397 , version 1

Citer

Xiyan He, Gilles Mourot, Didier Maquin, José Ragot, Pierre Beauseroy, et al.. One-class SVM in multi-task learning. Annual Conference of the European Safety and Reliability Association, ESREL 2011, Sep 2011, Troyes, France. pp.CDROM. ⟨hal-00593397⟩
129 Consultations
1289 Téléchargements

Partager

Gmail Facebook X LinkedIn More