Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data
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DOI: 10.1109/tnnls.2020.3016928
Abstract: Active learning is an important learning paradigm in machine learning and data mining, which aims to train effective classifiers with as few labeled samples as possible. Querying discriminative (informative) and representative samples are the state-of-the-art… read more here.
Keywords: discriminative representative; active learning; querying discriminative; learning ... See more keywords