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1 October 1991 Error probabilities of minimum-distance classifiers
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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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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