An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences
2014pp. 898–906
Citations Over TimeTop 10% of 2014 papers
Abstract
Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most benefit in the creation of a classifier. AL allows to learn accurate systems with much less annotated data than what is required by pure supervised learning algorithms, hence limiting the tedious effort of annotating a large collection of data.
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