@article{oai:mie-u.repo.nii.ac.jp:00005038,
 author = {Kimura, Fumitaka and 木村, 文隆 and Wakabayashi, Tetsushi and 若林, 哲史 and Miyake, Yasuji and 三宅, 康二},
 journal = {Research reports of the Faculty of Engineering, Mie University},
 month = {Dec},
 note = {application/pdf, This paper deals with limited sample based optimum classifier design and the theoretical evaluation of the mean error rate. Gaussian population with unknown parameters is assumed. The conditional density given a limited sample of the population is first derived, and its relationship to the multivariate t-distribution is shown. Then, the mean error rate of the optimum classifier is theoretically evaluated by the quadrature of the conditional density. To verify the optimality of the classifier and correctness of the mean error calculation, the results of Monte Carlo simulation employing a new sampling procedure are shown. The role of the a priori distribution in reducing the mean error rate is discussed at the end of this paper.},
 pages = {51--70},
 title = {Limited Sample Based Optimum Classifier Design and the Evaluation of the Mean Error Rate},
 volume = {23},
 year = {1998}
}