Estimating labels from label proportions
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.
|Collections||ANU Research Publications|
|Source:||Proceedings of The 25th International Conference on Machine Learning (ICML 2008)|
|01_Quadrianto_Estimating_labels_from_label_2008.pdf||465.84 kB||Adobe PDF||Request a copy|
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