Basics of feature selection and statistical learning for high-energy physics

Conference Paper

This 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.

Duke Authors

Cited Authors

  • Vossen, A

Published Date

  • December 1, 2008

Published In

  • Inverted Cern School of Computing, Icsc 2005 and Icsc 2006 Proceedings

Start / End Page

  • 1 - 12

International Standard Book Number 13 (ISBN-13)

  • 9789290833093

Citation Source

  • Scopus