Agnostic Active Learning Without Constraints

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چکیده

We present and analyze an agnostic active learning algorithm that works withoutkeeping a version space. This is unlike all previous approaches where a restrictedset of candidate hypotheses is maintained throughout learning, and only hypothe-ses from this set are ever returned. By avoiding this version space approach, ouralgorithm sheds the computational burden and brittleness associated with main-taining version spaces, yet still allows for substantial improvements over super-vised learning for classification.

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تاریخ انتشار 2010