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Abstract: We initiate the study of learning from multiple sources of limited data, each of which may be corrupted at a different rate. We develop a complete theory of which data sources should be used for two fundamental problems: estimating the bias of a coin, and learning a classifier in the presence of label noise. In both cases, efficient algorithms are provided for computing the optimal subset of data. (Update)
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BibTeX entry: (Update)
@misc{ michael-learning,
author = "Koby Crammer Michael",
title = "Learning from Data of Variable Quality",
url = "citeseer.ist.psu.edu/757704.html" }
Citations (may not include all citations):947 Statistical Learning Theory (context) - Vapnik - 1998
248 An Introduction to Computational Learning Theory (context) - Kearns, Vazirani - 1994
180 Combining labeled and unlabeled data with co-training - Blum, Mitchell - 1998
102 Towards efficient agnostic learning - Kearns, Schapire et al. - 1994
47 Rigorous learning curve bounds from statistical mechanics - Haussler, Kearns et al. - 1994
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