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Active Learning with Statistical Models

Author(s)
Cohn, David A.; Ghahramani, Zoubin; Jordan, Michael I.
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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. 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.
Date issued
1995-03-21
URI
http://hdl.handle.net/1721.1/7192
Other identifiers
AIM-1522
CBCL-110
Series/Report no.
AIM-1522CBCL-110
Keywords
AI, MIT, Artificial Intelligence, active learning, queries, locally weighted regression, LOESS, mixtures of gaussians, exploration, robotics

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  • AI Memos (1959 - 2004)
  • CBCL Memos (1993 - 2004)

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