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Abstract: Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training... (Update)
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BibTeX entry: (Update)
Lewis, D., and Catlett, J. 1994. Heterogeneous uncertainty sampling for supervised learning. In Machine Learning Proceedings of the 11th International Conference. http://citeseer.ist.psu.edu/135290.html More
@inproceedings{ lewis94heterogeneous,
author = "David D. Lewis and Jason Catlett",
title = "Heterogeneous uncertainty sampling for supervised learning",
booktitle = "Proceedings of {ICML}-94, 11th International Conference on Machine Learning",
publisher = "Morgan Kaufmann Publishers, San Francisco, US",
address = "New Brunswick, US",
editor = "William W. Cohen and Haym Hirsh",
pages = "148--156",
year = "1994",
url = "citeseer.ist.psu.edu/135290.html" }
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