Active Learning with Statistical Models

David A. Cohn, Zoubin Ghahramani, Michael I. Jordan

JAIR 1996 pp. 129-145

doi:10.1613/JAIR.295 /jair/1996/cohn1996jair-active/

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 locally weighted regression are both efficient and accurate.

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Text

Cohn et al. "Active Learning with Statistical Models." Journal of Artificial Intelligence Research, 1996. doi:10.1613/JAIR.295

Markdown

[Cohn et al. "Active Learning with Statistical Models." Journal of Artificial Intelligence Research, 1996.](https://mlanthology.org/jair/1996/cohn1996jair-active/) doi:10.1613/JAIR.295

BibTeX

@article{cohn1996jair-active,
  title     = {{Active Learning with Statistical Models}},
  author    = {Cohn, David A. and Ghahramani, Zoubin and Jordan, Michael I.},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {1996},
  pages     = {129-145},
  doi       = {10.1613/JAIR.295},
  volume    = {4},
  url       = {https://mlanthology.org/jair/1996/cohn1996jair-active/}
}