Online one-class machines based on the coherence criterion
Online one-class machines based on the coherence criterion
复制标题
基于一致性准则的在线一类机器
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Cédric Richard
中科院分区：
文献类型：
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作者：
Zineb Noumir;P. Honeine;Cédric Richard
本文研究了一种新的在线一类分类方法。我们考虑了一个最小二乘优化问题，其中模型的复杂性由一致性准则作为稀疏化规则来控制。该准则与一种简单的在线学习更新规则相结合，产生了一种计算要求较低的算法。在时间序列上进行的实验表明了我们的方法与现有方法的相关性。
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.