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|Title:||Active learning for probabilistic hypotheses using the maximum Gibbs error criterion|
|Source:||Nguyen, V.C.,Lee, W.S.,Ye, N.,Chai, K.M.A.,Chieu, H.L. (2013). Active learning for probabilistic hypotheses using the maximum Gibbs error criterion. Advances in Neural Information Processing Systems. ScholarBank@NUS Repository.|
|Abstract:||We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses. This objective function, called the policy Gibbs error, is the expected error rate of a random classifier drawn from the prior distribution on the examples adaptively selected by the active learning policy. Exact maximization of the policy Gibbs error is hard, so we propose a greedy strategy that maximizes the Gibbs error at each iteration, where the Gibbs error on an instance is the expected error of a random classifier selected from the posterior label distribution on that instance. We apply this maximum Gibbs error criterion to three active learning scenarios: non-adaptive, adaptive, and batch active learning. In each scenario, we prove that the criterion achieves near-maximal policy Gibbs error when constrained to a fixed budget. For practical implementations, we provide approximations to the maximum Gibbs error criterion for Bayesian conditional random fields and transductive Naive Bayes. Our experimental results on a named entity recognition task and a text classification task show that the maximum Gibbs error criterion is an effective active learning criterion for noisy models.|
|Source Title:||Advances in Neural Information Processing Systems|
|Appears in Collections:||Staff Publications|
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