Agnostic Active Learning Without Constraints
نویسندگان
چکیده
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.
منابع مشابه
Agnostic Active Learning Without Constraints
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 as...
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