ONLINE ONE-CLASS MACHINES BASED ON THE COHERENCE CRITERION

被引：0

作者：

Noumir, Zineb ^{[1
]}

Honeine, Paul ^{[1
]}

Richard, Cedric ^{[2
]}

机构：

[1] Univ Technol Troyes, CNRS, Inst Charles Delaunay, F-10010 Troyes, France

[2] Univ Nice, CNRS, Lab H Fizeau, F-06108 Nice, France

来源：

2012 PROCEEDINGS OF THE 20TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2012年

关键词：

support vector machines; kernel methods; one-class classification; online learning; coherence parameter;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：664 / 668

页数：5

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