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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, possibly with known label propor-tions. This problem occurs in areas like e-commerce, politics, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high prob-ability in a uniform convergence sense. Experiments show that our method works well in practice.

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
Source: Proceedings of The 25th International Conference on Machine Learning (ICML 2008)


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