Improving Generalization with Active Learning |
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Authors: | Cohn David Atlas Les Ladner Richard |
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Affiliation: | (1) Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 02139 Cambridge, MA;(2) Department of Electrical Engineering, University of Washington, 98195 Seattle, WA;(3) Department of Computer Science and Engineering, University of Washington, 98195 Seattle, WA |
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Abstract: | Active learning differs from learning from examples in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better generalization for a fixed number of training examples.In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning calledselective sampling and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers useful. We test our implementation, called anSG-network, on three domains and observe significant improvement in generalization.A preliminary version of this article appears as Cohn et al. (1990). |
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Keywords: | queries active learning generalization version space neural networks |
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