Proposes LCD and three other hybrid uncertainty-diversity sampling methods for active learning that outperform prior approaches by selecting uncertain yet diverse samples.
Incor- porating diversity and informativeness in multiple-instance active learning.IEEE transactions on fuzzy systems, 25(6):1460–1475, 2017
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Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
Proposes LCD and three other hybrid uncertainty-diversity sampling methods for active learning that outperform prior approaches by selecting uncertain yet diverse samples.