A study of SVM using a combination of the online learning method and the midpoint-validation method |
| |
Authors: | Hiroki Tamura Takeshi Yoshimatsu Shingo Yamashita Koichi Tanno |
| |
Affiliation: | 1.Department of Electrical and Electronic Engineering,University of Miyazaki,Miyazaki,Japan |
| |
Abstract: | The support vector machine (SVM) is known as one of the most influential and powerful tools for solving classification and
regression problems, but the original SVM does not have an online learning technique. Therefore, many researchers have introduced
online learning techniques to the SVM. In a previous article, we proposed an unsupervised online learning method using the
technique of the self-organized map for the SVM. In another article, we proposed the midpoint validation method for an improved
SVM. We test the performance of the SVM using a combination of the two techniques in this article. In addition, we compare
its performance with the original hard-margin SVM, the soft-margin SVM, and the k-NN method, and also experiment with our
proposed method on surface electromyogram recognition problems with changes in the position of the electrode. These experiments
showed that our proposed method gave a better performance than the other SVMs and corresponded to the changing data. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|