Poster

Near-Tight Margin-Based Generalization Bounds for Support Vector Machines

Allan Grønlund · Lior Kamma · Kasper Green Larsen

Keywords: [ Learning Theory ] [ Statistical Learning Theory ] [ Supervised Learning ]

[ Abstract ]
Tue 14 Jul 1 p.m. PDT — 1:45 p.m. PDT
Wed 15 Jul midnight PDT — 12:45 a.m. PDT

Abstract:

Support Vector Machines (SVMs) are among the most fundamental tools for binary classification.

In its simplest formulation, an SVM produces a hyperplane separating two classes of data using the largest possible margin to the data. The focus on maximizing the margin has been well motivated through numerous generalization bounds.

In this paper, we revisit and improve the classic generalization bounds in terms of margins. Furthermore, we complement our new generalization bound by a nearly matching lower bound, thus almost settling the generalization performance of SVMs in terms of margins.

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