Unbiased online active learning in data streams
KDD, pp. 195-203, 2011.
unlabeled samplesampling processoffline evaluationbinary classification problemclassification errorMore(13+)
Unlabeled samples can be intelligently selected for labeling to minimize classification error. In many real-world applications, a large number of unlabeled samples arrive in a streaming manner, making it impossible to maintain all the data in a candidate pool. In this work, we focus on binary classification problems and study selective la...More
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