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Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost. In this paper we introduce a new algorithm, KQBC, which is capable of actively learning large scale problems by using selective sampling. The algorithm overcomes the costly sampling step of the well known Query By Committee (QBC) algorithm by projecting onto a low dimensional space. KQBC also...
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ژورنال
عنوان ژورنال: Health Affairs
سال: 2020
ISSN: 0278-2715,1544-5208
DOI: 10.1377/hlthaff.2020.00389