NIPS Proceedingsβ

Agnostic Active Learning Without Constraints

Part of: Advances in Neural Information Processing Systems 23 (NIPS 2010)

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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.