Probability bounds for active learning in the regression problem

2012/12/18 by Fermin, Ana Karina, Ludeña, Carenne
#62J05 #62K05 #68T05 #FOS: Mathematics #Statistics Theory (math.ST)

paper · doi:10.48550/arxiv.1212.4457

Abstract

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.

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