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Poster
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
Allan Grønlund · Lior Kamma · Kasper Green Larsen

Tue Jul 14 01:00 PM -- 01:45 PM & Wed Jul 15 12:00 AM -- 12:45 AM (PDT) @
in Poster Session 7 »

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.

Keywords: Learning Theory · Statistical Learning Theory · Supervised Learning

Author Information

Allan Grønlund (Aarhus University)
Lior Kamma (Aarhus University)
Kasper Green Larsen (Aarhus University, MADALGO)

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