Basics of Feature Selection and Statistical Learning for High Energy Physics

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dc.creatorVossen, Anselm
dc.date2008-03-16
dc.date.accessioned2026-07-07T09:27:05Z
dc.date.available2026-07-07T09:27:05Z
dc.descriptionThis document introduces basics in data preparation, feature selection and learning basics for high energy physics tasks. The emphasis is on feature selection by principal component analysis, information gain and significance measures for features. As examples for basic statistical learning algorithms, the maximum a posteriori and maximum likelihood classifiers are shown. Furthermore, a simple rule based classification as a means for automated cut finding is introduced. Finally two toolboxes for the application of statistical learning techniques are introduced.
dc.description12 pages, 8 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006
dc.identifierhttps://arxiv.org/abs/0803.2344
dc.identifierhttp://arxiv.org/abs/0803.2344
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/156977
dc.subjectData Analysis, Statistics and Probability
dc.subjectHigh Energy Physics - Experiment
dc.titleBasics of Feature Selection and Statistical Learning for High Energy Physics
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