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Active learning with seed examples and search queries
April 14 @ 11:00 am
Active learning is a framework for supervised learning that explicitly models, and permits one to control and optimize, the costs of labeling data. The hope is that by carefully selecting which examples to label in an adaptive manner, the number of labels required to learn an accurate classifier is substantially reduced. However, in many learning settings (e.g., when some classes are rare), it is difficult to identify which examples are most informative to label, and existing active learning algorithms are prone to labeling uninformative examples.