COMPARISON OF CLASSIFIERS IN SMALL TRAINING SAMPLE-SIZE SITUATIONS FOR PATTERN-RECOGNITION

The main problem in statistical pattern recognition is to design a cla ssifier. Many researchers point out that a finite number of training s amples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, w e compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclid ean distance classifiers) when the number of training samples is small .