Learning from data of variable quality
Koby Crammer, Michael J. Kearns, Jennifer Russo Wortman · 1995
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 com-plete 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. 1