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arXiv · 1601.02809

Performance and optimization of support vector machines in high-energy physics classification problems

Mehmet Özgür Sahin·Dirk Krücker·Isabell-Alissandra Melzer-Pellmann
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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.

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Mehmet Özgür Sahin, Dirk Krücker, Isabell-Alissandra Melzer-Pellmann. 2016-01-14. Performance and optimization of support vector machines in high-energy physics classification problems. https://doi.org/10.1016/j.nima.2016.09.017

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