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Communication Dans Un Congrès Année : 2011

One-class SVM in multi-task learning

Résumé

Classical machine learning technologies have achieved much success in the learning of a single task at a time. However, in many practical applications we may need to learn a number of related tasks or to rebuild the model from new data, for example, in the problem of fault detection and diagnosis of a system that contains a set of equipments a priori identical but working under different conditions. Indeed, it is common to encounter in industrial problems a number of a priori identical plants, such as in the building or maintenance of a fleet of nuclear power plants or of a fleet of their components. In such cases, the learning of the behavior of each equipment can be considered as a single task, and it would be nice to transfer or leverage the useful information between related tasks. Therefore, 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. We present 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.
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Dates et versions

hal-00653752 , version 1 (20-12-2011)

Identifiants

  • HAL Id : hal-00653752 , version 1

Citer

Xiyan He, Gilles Mourot, Didier Maquin, José Ragot, Pierre Beauseroy, et al.. One-class SVM in multi-task learning. 4ème Workshop du Groupement d'Intérêt Scientifique "Surveillance, Sûreté, Sécurité des Grands Systèmes", Oct 2011, Valenciennes, France. pp.CDROM. ⟨hal-00653752⟩
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