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
收藏
页码：664 / 668
页数：5
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