Support Vector Machines
as Bayes’ Classifiers

Peter L. Jackson, Aviation Studies Institute (ASI)

Published in the Operations Research Letters (Volume 50, Issue 5), September 2022
https://doi.org/10.1016/j.orl.2022.06.003

Abstract

We reformulate the problem of determining support vectors directly as an application of Bayes’ classifiers rather than as the dual program to a binary geometric separation problem. The primary purpose of the reformulation is to create a simpler exposition of the support vector machines technique. A secondary advantage is that it immediately and naturally applies to multi-class classification problems where the kernel function can be normalized as a density.