PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating labels from label proportions
Novi Quadrianto, Alex Smola, Tiberio Caetano and quoc le
In: the 25th International Conference on Machine Learning, 05 July - 09 July 2008, Helsinki, Finland.

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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EPrint Type:Conference or Workshop Item (Talk)
Additional Information:Topics : Gaussian Processes, Classification and Prediction, Probabilistic Models, Transduction, Semi-supervised Learning
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4167
Deposited By:Novi Quadrianto
Deposited On:19 September 2008