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Reinforced Meta Active Learning. (arXiv:2203.04573v1 [cs.LG])
March 10, 2022, 2:11 a.m. | Michael Katz, Eli Kravchik
cs.LG updates on arXiv.org arxiv.org
In stream-based active learning, the learning procedure typically has access
to a stream of unlabeled data instances and must decide for each instance
whether to label it and use it for training or to discard it. There are
numerous active learning strategies which try to minimize the number of labeled
samples required for training in this setting by identifying and retaining the
most informative data samples. Most of these schemes are rule-based and rely on
the notion of uncertainty, which …
More from arxiv.org / cs.LG updates on arXiv.org
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