2024-03-29T10:41:52Z
https://tsukuba.repo.nii.ac.jp/oai
oai:tsukuba.repo.nii.ac.jp:00045601
2022-04-27T09:17:14Z
117:1697
117:786
3:62:5592:629
Support vector machine and its bias correction in high-dimension, low-sample-size settings
青嶋, 誠
矢田, 和善
Nakayama, Yugo
Yata, Kazuyoshi
Aoshima, Makoto
© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM holds a consistency property in which misclassification rates tend to zero as the dimension goes to infinity under certain severe conditions. We show that the SVM is very biased in HDLSS settings and its performance is affected by the bias directly. In order to overcome such difficulties, we propose a bias-corrected SVM (BC-SVM). We show that the BC-SVM gives preferable performances in HDLSS settings. We also discuss the SVMs in multiclass HDLSS settings. Finally, we check the performance of the classifiers in actual data analyses.
Elsevier
2017-12
eng
journal article
http://hdl.handle.net/2241/00150876
https://tsukuba.repo.nii.ac.jp/records/45601
10.1016/j.jspi.2017.05.005
03783758
AA00253748
Journal of statistical planning and inference
191
88
100
https://tsukuba.repo.nii.ac.jp/record/45601/files/JSPI_191.pdf
application/pdf
2.5 MB
2019-12-01