A theoretical framework for supervised learning from regions

Abstract

Supervised learning is investigated, when the data are represented not only by labeled points but also labeled regions of the input space. In the limit case, such regions degenerate to single points and the proposed approach changes back to the classical learning context. The adopted framework entails the minimization of a functional obtained by introducing… (More)
DOI: 10.1016/j.neucom.2012.06.065

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