PoS - Proceedings of Science
Volume 282 - 38th International Conference on High Energy Physics (ICHEP2016) - Poster Session
Support Vector Machines and generalisation in HEP
J. Hays,* A. Bevan, T.J. Stevenson
*corresponding author
Full text: pdf
Pre-published on: February 06, 2017
Published on: April 19, 2017
Abstract
The concept of Support Vector Machines is briefly reviewed and their potential use in high energy physics scenarios is discussed. They have the potential to be less susceptible to over-fitting than artificial neural networks and boosted decisions trees - whose use is common across high energy particle physics. Cross validation is discussed in the context of improving generalisation of machine learning algorithms and preliminary work on bringing more rigor to understanding generalisation in HEP analysis is presented.
DOI: https://doi.org/10.22323/1.282.0976
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.