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arXiv · 1011.5053

Tight Sample Complexity of Large-Margin Learning

Sivan Sabato·Nathan Srebro·Naftali Tishby
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Abstract

We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L_2 regularization: We introduce the \gamma-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 \gamma-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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BibTeXRIS

Sivan Sabato, Nathan Srebro, Naftali Tishby. 2010-11-23. Tight Sample Complexity of Large-Margin Learning. https://arxiv.org/abs/1011.5053

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