Support Vector Machines in High Energy Physics

2008/03/16 by A. Vossen, Anselm Vossen, Vossen, Anselm · 1 citation
Computer Science · Physics and Astronomy · #Algorithms and Data Compression #Anomaly Detection Techniques and Applications #Machine Learning and Data Classification #hep-ex #physics.data-an

paper · pdf · doi:10.48550/arxiv.0803.2345

11 pages, 12 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006

arxiv created 2008/03/16 · arxiv updated 2009/12/01

Abstract

This lecture will introduce the Support Vector algorithms for classification and regression. They are an application of the so called kernel trick, which allows the extension of a certain class of linear algorithms to the non linear case. The kernel trick will be introduced and in the context of structural risk minimization, large margin algorithms for classification and regression will be presented. Current applications in high energy physics will be discussed.

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