18–22 Jan 2016
UTFSM, Valparaíso (Chile)
Chile/Continental timezone

Support Vector Machines and generalisation in HEP

19 Jan 2016, 14:25
25m
UTFSM, Valparaíso (Chile)

UTFSM, Valparaíso (Chile)

Avenida España 1680, Valparaíso Chile
Oral Data Analysis - Algorithms and Tools Track 2

Speaker

Thomas James Stevenson (University of London (GB))

Description

We review the concept of support vector machines (SVMs) and discuss examples of their use in a number of scenarios. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than those other algorithms are to over training. This issue is related to the generalisation of a multivariate algorithm (MVA); a problem that has often been overlooked in particle physics. We discuss cross validation and how this can be used to improve the generalisation of a MVA in the context of High Energy Physics analyses. The examples presented use the Toolkit for Multivariate Analysis (TMVA) based on ROOT and describe our improvements to the SVM functionality and new tools introduced for cross validation within this framework.

Primary author

Adrian Bevan (University of London (GB))

Co-authors

Agni Bethani (University of London (GB)) Jonathan Hays (University of London (GB)) Thomas James Stevenson (University of London (GB))

Presentation materials

Peer reviewing

Paper