Abstract
We consider several complexity measures which capture the difficulty of learning under the i.i.d. assumption. Among these measures are growth function and VC dimension , covering number and fat-shattering dimension, and Rademacher complexity from statistical learning theory. Relationships among these complexity measures, their connection to learning, and tools for bounding them are provided. For each complexity measure, a uniform upper bound on the generalization error of classification problems is presented.
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