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Active Learning with Statistical Models
David A. Cohn
Zoubin Ghahramani
Michael I. Jordan
Center for Biological and Computational Learning
Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139 USA
Abstract:
For many types of machine learning algorithms, one can compute the
statistically ``optimal'' way to select training data. In this paper,
we review how optimal data selection 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
computationally expensive and approximate, the techniques for mixtures
of Gaussians and locally weighted regression are both efficient and
accurate. Empirically, we observe that the optimality criterion
sharply decreases the number of training examples the learner needs in
order to achieve good performance.
David Cohn
Mon Mar 25 09:20:31 EST 1996