Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. (arXiv:2104.13628v2 [cs.LG] UPDATED)

Modern machine learning systems such as deep neural networks are often highly
over-parameterized so that they can fit the noisy training data exactly, yet
they can still achieve small test errors in practice. In this paper, we study
this "benign overfitting" phenomenon of the maximum margin classifier for
linear classification problems. Specifically, we consider data generated from
sub-Gaussian mixtures, and provide a tight risk bound for the maximum margin
linear classifier in the over-parameterized setting. Our results precisely
characterize the …

arxiv classification maximum margin