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Distribution-Independent Reliable Learning
[article]
2014
arXiv
pre-print
We study several questions in the reliable agnostic learning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlier than others. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most ϵ, (ii) its false negative error rate is at most ϵ more than that of the best positive
arXiv:1402.5164v1
fatcat:tzmjjvdloveshhtm43rjgepjei