TY - GEN
T1 - Tight sample complexity of large-margin learning
AU - Sabato, Sivan
AU - Srebro, Nathan
AU - Tishby, Naftali
PY - 2010/1/1
Y1 - 2010/1/1
N2 - We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the γ-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 γ-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.
AB - We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the γ-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 γ-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.
UR - https://www.scopus.com/pages/publications/85161993206
M3 - Conference contribution
AN - SCOPUS:85161993206
SN - 9781617823800
T3 - Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
BT - Advances in Neural Information Processing Systems 23
PB - Neural Information Processing Systems
T2 - 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Y2 - 6 December 2010 through 9 December 2010
ER -