TMVA: The Toolkit for Multivariate Data Analysis eith ROOT

Multivariate classi cation methods based on machine learning techniques have become a fundamental ingredient to most physics analyses. The classi cation techniques themselves have also signi cantly evolved in recent years. Statisticians have found new ways to tune and to combine classi ers to furthe...

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Detalles Bibliográficos
Autores principales: Höcker, Andreas, Speckmayer, Peter, Stelzer, Jörg, Tegenfeldt, Fredrik, Voss, Helge
Lenguaje:eng
Publicado: CERN 2008
Materias:
Mathematical Physics and Mathematics
Detectors and Experimental Techniques
Computing and Computers
Acceso en línea:https://dx.doi.org/10.5170/CERN-2008-001.184
http://cds.cern.ch/record/1099990
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author Höcker, Andreas
Speckmayer, Peter
Stelzer, Jörg
Tegenfeldt, Fredrik
Voss, Helge
author_facet Höcker, Andreas
Speckmayer, Peter
Stelzer, Jörg
Tegenfeldt, Fredrik
Voss, Helge
author_sort Höcker, Andreas
collection CERN
description Multivariate classi cation methods based on machine learning techniques have become a fundamental ingredient to most physics analyses. The classi cation techniques themselves have also signi cantly evolved in recent years. Statisticians have found new ways to tune and to combine classi ers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit offering a large variety of multivariate classi cation algorithms. TMVA manages the simultaneous training, testing and performance evaluation of all the classi ers with a user-friendly interface, and also steers the application of the trained classi ers to data.
id cern-1099990
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2008
publisher CERN
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spelling cern-10999902019-09-30T06:29:59Zdoi:10.5170/CERN-2008-001.184http://cds.cern.ch/record/1099990engHöcker, AndreasSpeckmayer, PeterStelzer, JörgTegenfeldt, FredrikVoss, HelgeTMVA: The Toolkit for Multivariate Data Analysis eith ROOTMathematical Physics and MathematicsDetectors and Experimental TechniquesComputing and ComputersMultivariate classi cation methods based on machine learning techniques have become a fundamental ingredient to most physics analyses. The classi cation techniques themselves have also signi cantly evolved in recent years. Statisticians have found new ways to tune and to combine classi ers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit offering a large variety of multivariate classi cation algorithms. TMVA manages the simultaneous training, testing and performance evaluation of all the classi ers with a user-friendly interface, and also steers the application of the trained classi ers to data.CERNoai:cds.cern.ch:10999902008
spellingShingle Mathematical Physics and Mathematics
Detectors and Experimental Techniques
Computing and Computers
Höcker, Andreas
Speckmayer, Peter
Stelzer, Jörg
Tegenfeldt, Fredrik
Voss, Helge
TMVA: The Toolkit for Multivariate Data Analysis eith ROOT
title TMVA: The Toolkit for Multivariate Data Analysis eith ROOT
title_full TMVA: The Toolkit for Multivariate Data Analysis eith ROOT
title_fullStr TMVA: The Toolkit for Multivariate Data Analysis eith ROOT
title_full_unstemmed TMVA: The Toolkit for Multivariate Data Analysis eith ROOT
title_short TMVA: The Toolkit for Multivariate Data Analysis eith ROOT
title_sort tmva: the toolkit for multivariate data analysis eith root
topic Mathematical Physics and Mathematics
Detectors and Experimental Techniques
Computing and Computers
url https://dx.doi.org/10.5170/CERN-2008-001.184
http://cds.cern.ch/record/1099990
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AT tegenfeldtfredrik tmvathetoolkitformultivariatedataanalysiseithroot
AT vosshelge tmvathetoolkitformultivariatedataanalysiseithroot

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