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Proceedings Paper

Feature extraction using self-organizing networks
Author(s): Jochen Dahm; C. Voigt; K.-H. Becks; J. Drees
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Paper Abstract

Self-organizing networks are used to extract kinematical features and to study correlations of high dimensional variable spaces in new physics areas. This method is applied in the search for heavy neutrinos at LEP200. We show that the extracted knowledge improves the distinction between heavy neutrino candidates and background.

Paper Details

Date Published: 6 April 1995
PDF: 6 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205113
Show Author Affiliations
Jochen Dahm, Univ. Wuppertal (Germany)
C. Voigt, Univ. Wuppertal (Germany)
K.-H. Becks, Univ. Wuppertal (Germany)
J. Drees, Univ. Wuppertal (Germany)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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