Experimental study of performance of pattern classifiers and the size of design samples

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Abstract

This paper presents results of simulation experiments concerning the two class pattern classification problem assuming a multivariate normal population. Both a quadratic and a linear discriminant function designed by using estimated covariance matrices and mean vectors have limited performance compared to the optimal Bayes decision. Two approximate estimators of the amount of degradation in the recognition rate were proposed by Raudys and Fukunaga, respectively. This paper presents the experimental evaluation of the goodness of these estimators. We show quantitatively how well those estimators work and also confirm that the modified classifier designed by using Stein's estimator achieves better performance than the conventional one. © 1995.

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Takeshita, T., & Toriwaki, J. ichiro. (1995). Experimental study of performance of pattern classifiers and the size of design samples. Pattern Recognition Letters, 16(3), 307–312. https://doi.org/10.1016/0167-8655(94)00099-O

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