Error probabilities of minimum-distance classifiers

Helene Poublan; Francis Castanie

doi:10.1117/12.48391

1 October 1991 Error probabilities of minimum-distance classifiers

Helene Poublan, Francis Castanie

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48391
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Helene Poublan and Francis Castanie "Error probabilities of minimum-distance classifiers", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48391

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