Estimation of Classification Error

Authors:
K. Fukunaga;D. L. Kessell
Affiliations:
-;-
Venue:
IEEE Transactions on Computers
Year:
1971

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Abstract

This paper discusses methods of estimating the probability of error for the Bayes' classifier which must be designed and tested with a finite number of classified samples. The expected difference between estimates is discussed. A simplifled algorithm to compute the leaving-one-out method is proposed for multivariate normal distributions wtih unequal co-variance matrices. The discussion is extended to nonparametric classifiers by using the Parzen approximation for the density functions. Experimental results are shown for both parametric and nonparametric cases.