Performance and optimization of support vector machines in high-energy physics classification problems
High Energy Physics - Experiment
2022-11-16 v2
Abstract
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.
Keywords
Cite
@article{arxiv.1601.02809,
title = {Performance and optimization of support vector machines in high-energy physics classification problems},
author = {Mehmet Özgür Sahin and Dirk Krücker and Isabell-Alissandra Melzer-Pellmann},
journal= {arXiv preprint arXiv:1601.02809},
year = {2022}
}
Comments
20 pages, 6 figures