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We investigate agnostic learning when there is no noise in the labeling function, that is, the labels are deterministic. We show that in this setting, in contrast to the fully agnostic learning setting (with possibly noisy labeling functions), the sample complexity of learning a binary hypothesis class is not fully determined by the VCdimension of the class. For any d, we present classes of VC-dimension d that are learnable from O(d/ ) many samples and classes that require samples of sizes Ω(d/dblp:conf/isaim/Ben-DavidU14 fatcat:jgzamnuswrb2jjuf6m6z3ooskm