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Estimating Labels from Label Proportions

Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le; 10(82):2349−2374, 2009.

Abstract

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 proportions. 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 probability in a uniform convergence sense. Experiments show that our method works well in practice.

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