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CORR
2010
Springer

Agnostic Active Learning Without Constraints

12 years 1 months ago
Agnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, our algorithm sheds the computational burden and brittleness associated with maintaining version spaces, yet still allows for substantial improvements over supervised learning for classification.
Alina Beygelzimer, Daniel Hsu, John Langford, Tong
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CORR
Authors Alina Beygelzimer, Daniel Hsu, John Langford, Tong Zhang
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