Unsupervised Active Learning in Large Domains
نویسندگان
چکیده
Active learning is a powerful approach to an alyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to which the query is being optimized. The standard information gain, for example, does not permit an accurate evaluation with a small committee, a representative subset of the model space. We propose a surrogate measure requiring only a small committee and discuss the properties of this new mea sure. We devise, in addition, a bootstrap approach for committee selection. The ad vantages of this approach are illustrated in the context of recovering (regulatory) net
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