81 citations · 343 across the 26 of their papers we have counts for
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cs.LG2018★ 13 cited
Impact of Batch Size on Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considera…
cs.LG2018★ 23 cited
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection
Michael Bloodgood
This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as informat…