@inproceedings{oai:mie-u.repo.nii.ac.jp:00010048,
 month = {Jan},
 note = {application/pdf, In this paper, we describe a comparative study on techniques of feature transformation and classification to improve the accuracy of automatic text classification. The normalization to the relative word frequency, the principal component analysis (K-L transformation) and the power transformation were applied to the feature vectors, which were classified by the Euclidean distance, the linear discriminant function, the projection distance, the modified projection distance and the SVM. In order to improve the classification accuracy, the multi-classifier combination by majority vote was employed., Berlin ; New York, 501, Content computing : Advanced Workshop on Content Computing, AWCC 2004, ZhenJiang, JiangSu, China, November 15-17, 2004 : proceedings, Lecture Notes in Computer Science},
 pages = {458--463},
 publisher = {Springer},
 title = {Accuracy improvement of automatic text classification based on feature transformation and multi-classifier combination},
 volume = {3309},
 year = {2004}
}