20 citations · 62 across the 6 of their papers we have counts for
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The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training da…
Stopping Active Learning based on Predicted Change of F Measure for Text Classification
Michael Altschuler, Michael Bloodgood
During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted…
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…
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…
Analysis of Stopping Active Learning based on Stabilizing Predictions
Michael Bloodgood, John Grothendieck
Within the natural language processing (NLP) community, active learning has been widely investigated and applied in order to alleviate the annotation bottleneck faced by developers…