文章基本信息

标题：Strong Convex Loss Can Increase the Learning Rates of Online Learning
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作者：Sheng, Baohuai ; Duan, Liqin ; Ye, Peixin 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2014
卷号：9
期号：7
页码：1606-1611
DOI：10.4304/jcp.9.7.1606-1611
语种：English
出版社：Academy Publisher
摘要：It is known that kernel regularized online learning has the advantages of low complexity and simple calculations, and thus is accompanied with slow convergence and low accuracy. Often the algorithm are designed with the help of gradient of the loss function, the complexity of the loss may influence the convergence. In this paper, we show, at some extent, the strong convexity can increase the learning rates.
关键词：Online learning;Strong convex loss;Learning rates