Estimating labels from label proportions
Date
2009
Authors
Quadrianto, Novi
Smola, Alexander
Caetano, Tiberio
Le, Quoc Viet
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Publisher
MIT Press
Abstract
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.
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Keywords
Keywords: Consistent estimators; Content detections; E commerces; Following problems; High probabilities; Spam filtering; Uniform convergences; Electronic commerce; Learning systems; Robot learning; Labels
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Source
Journal of Machine Learning Research
Type
Journal article
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Restricted until
2037-12-31
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