@inproceedings{oai:nagasaki-u.repo.nii.ac.jp:00020367,
 author = {Hotta, Seiji and Kiyasu, Senya and Miyahara, Sueharu},
 book = {Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04)},
 month = {},
 note = {application/pdf, The typical nonparametric method of pattern recognition "k-nearest neighbor rule (kNN)" is carried out by counting the labels of k-nearest training samples to a test sample. This method collects the k-nearest neighbors without taking into account a class, and it outputs the class of the test sample by using only the labels of neighborhoods. This paper presents a classifier that outputs the class of a test sample by measuring the distance between the test sample and the average patterns, which are calculated using the k-nearest neighbors belonging to individual classes. A kernel method can be applied to this classifier for improving recognition rates. The performance of the proposed method is verified by experiments with benchmark data sets., text, ICPR 2004, August 23-26, 2004, Cambridge, UK, 17th International Conference on Pattern Recognition (ICPR'04) V.4 pp. 412-415 ; 2004},
 pages = {412--415},
 publisher = {IEEE Computer Society Press},
 title = {Pattern Recognition Using Average Patterns of Categorical k-Nearest Neighbors},
 volume = {4},
 year = {2004}
}