Active Learning with Statistical Models
Abstract
For many types of learners one can compute the statistically "op(cid:173) timal" 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 locally weighted regres(cid:173) sion are both efficient and accurate.
Cite
Text
Cohn et al. "Active Learning with Statistical Models." Neural Information Processing Systems, 1994.Markdown
[Cohn et al. "Active Learning with Statistical Models." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/cohn1994neurips-active/)BibTeX
@inproceedings{cohn1994neurips-active,
title = {{Active Learning with Statistical Models}},
author = {Cohn, David A. and Ghahramani, Zoubin and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {705-712},
url = {https://mlanthology.org/neurips/1994/cohn1994neurips-active/}
}