Convergence analysis of online algorithms

被引：21
|
作者：
Ying, Yiming [1 ]
机构：
[1] City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China
来源：
ADVANCES IN COMPUTATIONAL MATHEMATICS | 2007年 / 27卷 / 03期
关键词：
online learning algorithm; reproducing kernel Hilbert space; regularized sample error; general loss function;
D O I：
10.1007/s10444-005-9002-z
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we are interested in the analysis of regularized online algorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and step sizes are given to ensure convergence. Explicit learning rates are also given for particular step sizes.
引用
收藏
页码：273 / 291
页数：19
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