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Towards anytime active learning
2013
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics - IDEA '13
Many active learning methods use annotation cost or expert quality as part of their framework to select the best data for annotation. While these methods model expert quality, availability, or expertise, they have no direct influence on any of these elements. We present a novel framework built upon decision-theoretic active learning that allows the learner to directly control label quality by allocating a time budget to each annotation. We show that our method is able to improve performance
doi:10.1145/2501511.2501524
dblp:conf/kdd/Ramirez-LoaizaC13
fatcat:nvcanpnmynb7vivyc65rd6qpt4