Support Vector Machines in High Energy Physics
| dc.creator | Vossen, Anselm | |
| dc.date | 2008-03-16 | |
| dc.date.accessioned | 2026-07-07T09:27:05Z | |
| dc.date.available | 2026-07-07T09:27:05Z | |
| dc.description | 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. | |
| dc.description | 11 pages, 12 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006 | |
| dc.identifier | https://arxiv.org/abs/0803.2345 | |
| dc.identifier | http://arxiv.org/abs/0803.2345 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/156978 | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.subject | High Energy Physics - Experiment | |
| dc.title | Support Vector Machines in High Energy Physics | |
| dc.type | text |