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Abstract: For many types of learners one can compute the statistically "optimal " way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and ... (Update)
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BibTeX entry: (Update)
Cohn, D. A.; Ghahramani, Z.; and Jordan, M. I. 1995. Active learning with statistical models. In Tesauro, G.; Touretzky, D.; and Alspector, J., eds., Advances in Neural Information Processing, volume 7. Morgan Kaufmann. http://citeseer.ist.psu.edu/article/cohn95active.html More
@inproceedings{ cohn95active,
author = "David A. Cohn and Zoubin Ghahramani and Michael I. Jordan",
title = "Active Learning with Statistical Models",
booktitle = "Advances in Neural Information Processing Systems",
volume = "7",
publisher = "The {MIT} Press",
editor = "G. Tesauro and D. Touretzky and T. Leen",
pages = "705--712",
year = "1995",
url = "citeseer.ist.psu.edu/article/cohn95active.html" }
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