Minimal Support Vector Machine

TL;DR

This paper introduces a Minimal SVM that employs an L0.5 norm on slack variables, effectively reducing support vectors and enhancing classification performance over traditional SVMs.

Contribution

It proposes a novel SVM formulation using L0.5 norm on slack variables, improving support vector reduction and classification accuracy.

Findings

Reduces the number of support vectors compared to traditional SVMs

Increases classification performance with the new formulation

Demonstrates effectiveness through experimental results

Abstract

Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes. This hyperplane is determined by support vectors. In existing SVM formulations, the objective function uses L2 norm or L1 norm on slack variables. The number of support vectors is a measure of generalization errors. In this work, we propose a Minimal SVM, which uses L0.5 norm on slack variables. The result model further reduces the number of support vectors and increases the classification performance.