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Estimating labels from label proportions

Quadrianto, Novi; Smola, Alexander; Caetano, Tiberio; Le, Quoc Viet


Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known label proportions. This problem appears in areas like e-commerce, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high probability in a uniform convergence sense. Experiments show that our method works well in practice.

CollectionsANU Research Publications
Date published: 2009
Type: Journal article
Source: Journal of Machine Learning Research


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