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Proceedings Paper

Error probabilities of minimum-distance classifiers
Author(s): Helene Poublan; Francis Castanie
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Paper Abstract

In the Gaussian case, the Bayes classifiers and the minimum distance classifiers are compared. The comparison is based on the error probability and on the bias introduced by the estimation of the law parameters. It is shown, both theoretically and by simulations, that even a suboptimal use of the minimum distance classifiers may be justified when the finite design sample size is small with regard to dimensionality. An application to signal classification is studied where the best modelization of the signal is not the best representation for classification.

Paper Details

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48391
Show Author Affiliations
Helene Poublan, ENSEEIHT/GAPSE (France)
Francis Castanie, ENSEEIHT/GAPSE (France)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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