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In this paper, we propose a framework to build prediction models from data streams which contain both labeled and unlabeled examples. We argue that due to the increasing data collection ability but limited resources for labeling, stream data collected at hand may only have a small number of labeled examples, whereas a large portion of data remain unlabeled but can be beneficial for learning. Unleashing...
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