Name
Title | ||
|---|---|---|
Unsupervised learning method for a support vector machine and its application to surface electromyogram recognition |
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 this article, we propose an unsupervised online learning method using a self-organized
map for a SVM. Furthermore, the proposed method has a technique for the reconstruction of a SVM. We compare its performance
with the original SVM, the supervised learning method for the SVM, and a neural network, and also test our proposed method
on surface electromyogram recognition problems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10015-009-0682-1 | Artificial Life and Robotics |
Keywords | DocType | Volume |
Surface electromyogram,Support vector machine,Self-organizing map,Pattern classification problem | Journal | 14 |
Issue | ISSN | Citations |
3 | 1614-7456 | 2 |
PageRank | References | Authors |
0.42 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Tamura | 1 | 72 | 21.29 |
| Shuji Kawano | 2 | 10 | 1.02 |
| Koichi Tanno | 3 | 57 | 22.05 |