Convergence of online learning algorithm with a parameterized loss

AIMS Mathematics

article

Shuhua Wang¹ 

[1] School of Information Engineering, Jingdezhen Ceramic University

关键词: online learning;    parameterized loss;    convergence rate;    reproducing Hilbert space;    convex analysis;   

DOI  :  10.3934/math.20221098

学科分类：地球科学(综合)

来源: AIMS Press

【 摘 要 】

The research on the learning performance of machine learning algorithms is one of the important contents of machine learning theory, and the selection of loss function is one of the important factors affecting the learning performance. In this paper, we introduce a parameterized loss function into the online learning algorithm and investigate the performance. By applying convex analysis techniques, the convergence of the learning sequence is proved and the convergence rate is provided in the expectation sense. The analysis results show that the convergence rate can be greatly improved by adjusting the parameter in the loss function.

【 授权许可】

CC BY