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Human-in-the-Loop Active Learning with Multiple Query Schemes

An active-learning framework that decides not only which unlabeled example an expert should label but also how to query it, integrating full-information and cheaper partial-information query types through a probabilistic model, and adding a data-driven exploration-exploitation rule embeddable in existing active-learning algorithms to cut the cost of expert labeling.