Efficient Active Learning

author: Nikos Karampatziakis, Department of Computer Science, Cornell University
published: July 25, 2011,   recorded: July 2011,   views: 4519


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We present and analyze an active learning algorithm that is theoretically sound in an agnostic setting, empirically effective, and as efficient as standard online learning algorithms. This allows us to soundly and effectively optimize the explore/exploit trade-off in active learning at a scale of 10^6 examples/second. The present work is primarily based on (Beygelzimer et al., 2010) and (Karampatziakis & Langford, 2011).

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