In this article we consider the problem of choosing an optimal sampling scheme for the regression problem simultaneously with that of model selection. We consider a batch type approach and an on-line approach following algorithms recently developed for the classification problem. Our main tools are concentration-type inequalities which allow us to bound the supremum of the deviations of the sampling scheme corrected by an appropriate weight function.
@article{arxiv.1212.4457,
title = {Probability bounds for active learning in the regression problem},
author = {Ana Karina Fermin and Carenne Ludeña},
journal= {arXiv preprint arXiv:1212.4457},
year = {2018}
}