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Abstract: Active learning differs from passive "learning from examples" in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active... (Update)
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BibTeX entry: (Update)
Cohn, D.; Atlas, L.; and Ladner, R. 1994. Improving generalization with active learning. Machine Learning 15. http://citeseer.ist.psu.edu/cohn92improving.html More
@article{ cohn94improving,
author = "David A. Cohn and Les Atlas and Richard E. Ladner",
title = "Improving Generalization with Active Learning",
journal = "Machine Learning",
volume = "15",
number = "2",
pages = "201-221",
year = "1994",
url = "citeseer.ist.psu.edu/cohn92improving.html" }
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