Support Vector Machines in High Energy Physics
Data Analysis, Statistics and Probability
2008-03-18 v1 High Energy Physics - Experiment
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.
Keywords
Cite
@article{arxiv.0803.2345,
title = {Support Vector Machines in High Energy Physics},
author = {Anselm Vossen},
journal= {arXiv preprint arXiv:0803.2345},
year = {2008}
}
Comments
11 pages, 12 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006