An experimental study of learning curves for statistical pattern classifiers

Statistical pattern classifiers are designed by population parameters of pattern distributions estimated by a set of training samples. Therefore, classification performance depends considerably on training sample size. Learning curves exhibit asymptotic behaviors where a probability of misclassification decreases as a number of training samples increases. This paper presents asymptotic behaviors of effects of training sample size and shows that learning curves for practical purpose can be obtained using available samples