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The Optimal Sample Complexity of PAC Learning (1507.00473v4)
Published 2 Jul 2015 in cs.LG and stat.ML
Abstract: This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity of PAC learning. The technique and analysis build on a recent breakthrough by Hans Simon.
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