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Creators/Authors contains: "Conrad Holtsclaw, Meet P."

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  1. (, The ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop on Data Collection, Curation, and Labeling for Mining and Learning)
    Stream-based active learning methods assume that data instances arrive in sequence and the decision must be made to query an instance or not as it arrives. In mobile health and human activity recognition, the data stream is often block-structured where instances in the same block have the same label, but the boundaries between blocks are unobserved. In this paper, we propose an approach to active learning in this setting where we simultaneously learn to segment the stream while learning an instance-level discriminative classifier. We show that by propagating collected labels into inferred segments, we can learn improved predictive models with significantly fewer queries. 
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    Accepted Manuscript Full Text Available