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Communication Dans Un Congrès Année : 2012

Online One-Class Machines Based on the Coherence Criterion

Résumé

In this paper, we investigate a novel online one-class classification method. We consider a least-squares optimization problem, where the model complexity is controlled by the coherence criterion as a sparsification rule. This criterion is coupled with a simple updating rule for online learning, which yields a low computational demanding algorithm. Experiments conducted on time series illustrate the relevance of our approach to existing methods.
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Dates et versions

hal-01966019 , version 1 (27-12-2018)

Identifiants

  • HAL Id : hal-01966019 , version 1

Citer

Zineb Noumir, Paul Honeine, Cédric Richard. Online One-Class Machines Based on the Coherence Criterion. Proc. 20th European Conference on Signal Processing (EUSIPCO), 2012, Bucharest, Romania. pp.664 - 668. ⟨hal-01966019⟩
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