Abstract
In this paper, we discuss the effects of the sample size on the generalization ability of Parzen classifiers. When the sizes of samples per class are much unequal, the performance of the Parzen classifier is further degraded. In order to overcome this problem, we propose to use the Toeplitz estimator and bootstrap samples in designing Parzen classifiers. Experimental results show that these techniques are very effective means for designing Parzen classifiers, particularly when the sizes of samples per class are much unequal.
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