Tight Generalization Bounds for Large-Margin Halfspaces

TL;DR

This paper establishes the first asymptotically tight generalization bounds for large-margin halfspaces, linking margin, training data fraction, failure probability, and training size.

Contribution

It provides the first tight theoretical bounds for large-margin halfspaces, improving understanding of their generalization capabilities.

Findings

Bound is asymptotically tight in key parameters

Clarifies the tradeoff between margin and generalization

Enhances theoretical understanding of large-margin classifiers

Abstract

We prove the first generalization bound for large-margin halfspaces that is asymptotically tight in the tradeoff between the margin, the fraction of training points with the given margin, the failure probability and the number of training points.