Learning from Ambiguously Labeled Examples

Abstract

Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete… (More)
DOI: 10.1007/11552253_16

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