Square Root Penalty: Adaptation to the Margin in Classification and in Edge Estimation
统计理论
2007-06-13 v1 统计理论
摘要
We consider the problem of adaptation to the margin in binary classification. We suggest a penalized empirical risk minimization classifier that adaptively attains, up to a logarithmic factor, fast optimal rates of convergence for the excess risk, that is, rates that can be faster than n^{-1/2}, where n is the sample size. We show that our method also gives adaptive estimators for the problem of edge estimation.
引用
@article{arxiv.math/0507422,
title = {Square Root Penalty: Adaptation to the Margin in Classification and in Edge Estimation},
author = {A. B. Tsybakov and S. A. van de Geer},
journal= {arXiv preprint arXiv:math/0507422},
year = {2007}
}
备注
Published at http://dx.doi.org/10.1214/009053604000001066 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)