Tight Sample Complexity of Large-Margin Learning
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Abstract
We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with 
regularization: We introduce the 
-adapted-dimension, which is a simple function of the spectrum of a distribution’s covariance matrix, and show distribution-specific upper and lower bounds on the sample complexity, both governed by the 
-adapted-dimension of the source distribution. We conclude that this new quantity tightly characterizes the true sample complexity of large-margin classification. The bounds hold for a rich family of sub-Gaussian distributions.
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