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Support Vector Machines in Analysis of Top Quark Production

A. Vaiciulis
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Abstract

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. The Support Vector Machine learning algorithm is a relatively new way to solve pattern recognition problems and has several advantages over methods such as neural networks. The SVM approach is described and compared to a conventional analysis for the case of identifying top quark signal events in the dilepton decay channel amidst a large number of background events.

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A. Vaiciulis. 2002-05-21. Support Vector Machines in Analysis of Top Quark Production. https://doi.org/10.1016/s0168-9002(03)00479-0

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