Importance Weighted Active Learning

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dc.creatorBeygelzimer, Alina
dc.creatorDasgupta, Sanjoy
dc.creatorLangford, John
dc.date2008-12-29
dc.date2009-05-20
dc.date.accessioned2026-07-07T13:16:18Z
dc.date.available2026-07-07T13:16:18Z
dc.descriptionWe present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. Experiments on passively labeled data show that this approach reduces the label complexity required to achieve good predictive performance on many learning problems.
dc.identifierhttps://arxiv.org/abs/0812.4952
dc.identifierhttp://arxiv.org/abs/0812.4952
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/230749
dc.subjectMachine Learning
dc.titleImportance Weighted Active Learning
dc.typetext

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