Active learning with statistical models

Authors:
David A. Cohn;Zoubin Ghahramani;Michael I. Jordan
Affiliations:
Center for Biological and Computational Learning, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA;Center for Biological and Computational Learning, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA;Center for Biological and Computational Learning, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
Venue:
Journal of Artificial Intelligence Research
Year:
1996

Citing 11
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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.