Sampling discretization error for function classes
Numerical Analysis
2018-12-20 v1
Abstract
The new ingredient of this paper is that we consider infinitely dimensional classes of functions and instead of the relative error setting, which was used in previous papers on norm discretization, we consider the absolute error setting. We demonstrate how known results from two areas of research -- supervised learning theory and numerical integration -- can be used in sampling discretization of the square norm on different function classes.
Cite
@article{arxiv.1812.08100,
title = {Sampling discretization error for function classes},
author = {Vladimir Temlyakov},
journal= {arXiv preprint arXiv:1812.08100},
year = {2018}
}