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Support Vector Machines and Generalisation in HEP

Abstract : We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.
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Contributor : Emmanuelle Vernay <>
Submitted on : Wednesday, November 2, 2016 - 8:50:12 AM
Last modification on : Thursday, August 6, 2020 - 3:09:30 AM

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A. Bethani, J. Bevan, J. Hays, T. J. Stevenson. Support Vector Machines and Generalisation in HEP. 17th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT 2016), Jan 2016, Valparaíso Chile. pp.012052, ⟨10.1088/1742-6596/762/1/012052⟩. ⟨in2p3-01390480⟩



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