2010
NIPS
NeurIPS 2010
Tight Sample Complexity of Large-Margin Learning
Abstract
We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 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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— large-margin classification
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Hot Topic Early Bird
— sample complexity
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— Deep Learning and Machine Learning
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— Theory
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Learning Types > Classification
Deep Learning > Optimization & Theory > Theory
Machine Learning > Optimization & Theory > Sample Complexity