Online Regularized Learning Algorithms in RKHS with $β$- and $ϕ$-Mixing Sequences
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912472117542912 |
|---|---|
| author | Roy, Priyanka Saminger-Platz, Susanne |
| author_facet | Roy, Priyanka Saminger-Platz, Susanne |
| contents | 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 \(ϕ\)- and \(β\)-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Regularized Learning Algorithms in RKHS with $β$- and $ϕ$-Mixing Sequences Roy, Priyanka Saminger-Platz, Susanne Machine Learning Functional Analysis 60J20, 68T05, 68Q32, 62L20, 62H05 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 \(ϕ\)- and \(β\)-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. |
| title | Online Regularized Learning Algorithms in RKHS with $β$- and $ϕ$-Mixing Sequences |
| topic | Machine Learning Functional Analysis 60J20, 68T05, 68Q32, 62L20, 62H05 |
| url | https://arxiv.org/abs/2507.05929 |