I. Guyon, G. Cawley, G. Dror & V. Lemaire;
JMLR W&CP 16:19–45, 2011.
Results of the Active Learning Challenge
We organized a machine learning challenge on “active learning”, addressing problems
where labeling data is expensive, but large amounts of unlabeled data are available at low cost.
Examples include handwriting and speech recognition, document classiﬁcation, vision tasks, drug
design using recombinant molecules and protein engineering. The algorithms may place a limited
number of queries to get new sample labels. The design of the challenge and its results are
summarized in this paper and the best contributions made by the participants are included in
these proceedings. The website of the challenge remains open as a resource for students and
Page last modified on Wed Mar 30 11:09:05 2011.