Online Regularized Learning Algorithms in RKHS with $\beta$- and $\phi$-Mixing Sequences
Machine Learning
2025-07-09 v1 Machine Learning
Functional Analysis
Abstract
In this paper, we study an online regularized learning algorithm in a reproducing kernel Hilbert spaces (RKHS) based on a class of dependent processes. We choose such a process where the degree of dependence is measured by mixing coefficients. As a representative example, we analyze a strictly stationary Markov chain, where the dependence structure is characterized by the - and -mixing coefficients. Under these assumptions, we derive probabilistic upper bounds as well as convergence rates for both the exponential and polynomial decay of the mixing coefficients.
Cite
@article{arxiv.2507.05929,
title = {Online Regularized Learning Algorithms in RKHS with $\beta$- and $\phi$-Mixing Sequences},
author = {Priyanka Roy and Susanne Saminger-Platz},
journal= {arXiv preprint arXiv:2507.05929},
year = {2025}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2502.03551