Noisy classification with boundary assumptions
Statistics Theory
2013-07-15 v1 Statistics Theory
Abstract
We address the problem of classification when data are collected from two samples with measurement errors. This problem turns to be an inverse problem and requires a specific treatment. In this context, we investigate the minimax rates of convergence using both a margin assumption, and a smoothness condition on the boundary of the set associated to the Bayes classifier. We establish lower and upper bounds (based on a deconvolution classifier) on these rates.
Cite
@article{arxiv.1307.3369,
title = {Noisy classification with boundary assumptions},
author = {Sébastien Loustau and Clément Marteau},
journal= {arXiv preprint arXiv:1307.3369},
year = {2013}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1201.3283