Approximating Reproducing Kernel Hilbert Space Functions by Bernstein Operators
Abstract Motivated by kernel methods in machine learning theory, we study the uniform approximation of functions from reproducing kernel Hilbert spaces by Bernstein operators. Rates of approximation are provided in terms of the function norm in the reproducing kernel Hilbert space. A case study of contracting properties of the Bernstein operators is also presented.
Authors
- Feng Han (ORCID: https://orcid.org/0000-0003-2933-6205)
- Ruohan Shen (ORCID: https://orcid.org/0000-0001-7291-0896)
- Sonia Y. W. Hui (ORCID: https://orcid.org/0000-0001-8113-7946)
Institutions
- City University of Hong Kong (HK)
- Xinjiang University (CN)
Publication Details
- Journal
- Results in Mathematics
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1007/s00025-024-02253-w
- Citations
- 1
- Primary Topic
- Approximation Theory and Sequence Spaces
- Type
- article
- Field-Weighted Citation Impact
- 0.71