Feature extraction using self-organizing networks

Jochen Dahm; C. Voigt; K.-H. Becks; J. Drees

doi:10.1117/12.205113

6 April 1995 Feature extraction using self-organizing networks

Jochen Dahm, C. Voigt, K.-H. Becks, J. Drees

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205113
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Jochen Dahm, C. Voigt, K.-H. Becks, and J. Drees "Feature extraction using self-organizing networks", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205113

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