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Abstract: In many real-world learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper proposes a general method for efficiently training probabilistic classifiers, by selecting for training only the more informative examples in a stream of unlabeled examples. The method, committee-based sampling, evaluates the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn... (Update)
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BibTeX entry: (Update)
Dagan, I., and Engelson, S. 1995. Committee-based sampling for training probabilistic classifiers. In Proc. http://citeseer.ist.psu.edu/17150.html More
@inproceedings{ dagan95committeebased,
author = "Ido Dagan and Sean P. Engelson",
title = "Committee-Based Sampling For Training Probabilistic Classifiers",
booktitle = "International Conference on Machine Learning",
pages = "150-157",
year = "1995",
url = "citeseer.ist.psu.edu/17150.html" }
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