Convergence of online learning algorithm with a parameterized loss

• Published in: AIMS mathematics Vol. 7; no. 11; pp. 20066 - 20084
• Main Author: Wang, Shuhua
• Format: Journal Article
• Language: English
• Published: AIMS Press 01-01-2022
• Subjects: convergence rate; convex analysis; online learning; parameterized loss; reproducing hilbert space
• ISSN: 2473-6988; 2473-6988
• DOI: 10.3934/math.20221098

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.