KERNEL-BASED ONLINE GRADIENT DESCENT USING DISTRIBUTED APPROACH

被引：8
|
作者：
Chen, Xiaming [1 ]
机构：
[1] Shantou Univ, 243 Daxue Rd, Shantou, Guangdong, Peoples R China
来源：
MATHEMATICAL FOUNDATIONS OF COMPUTING | 2019年 / 2卷 / 01期
关键词：
Learning theory; distributed learning; online gradient descent;
D O I：
10.3934/mfc.2019001
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper we study the kernel-based online gradient descent with least squares loss without an explicit regularization term. Our approach is novel by controlling the expectation of the K-norm of f(t) using an iterative process. Then we use distributed learning to improve our result.
引用
收藏
页码：1 / 9
页数：9
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