Improving Generalization with Active Learning (1992) [232 citations — 1 self]
http://storm.cis.fordham.edu/~gweiss/dmrg/papers/c
http://www.cs.cmu.edu/~cohn/psyche/selsampling.ps.
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author = {David Cohn and Les Atlas and Richard Ladner},
title = {Improving Generalization with Active Learning},
year = {1992}
}
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Abstract:
Active learning differs from passive "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 that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active concept learning called selective 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 an SG-network, on three domains, and observe significant improvement in generalization.
