Summary
International Symposium on Nonlinear Theory and its Applications
2010
Session Number:A3L-D
Session:
Number:A3L-D1
Support Vector Machines with Online Unsupervised Learning Method and its Application to surface-Electromyogram Recognition Problems
Hiroki Tamura, Takeshi Yoshimatu, Koichi Tanno,
pp.177-180
Publication Date:2010/9/5
Online ISSN:2188-5079
Summary:
The support vector machine (abbr. 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 paper, we propose the new online unsupervised learning method using a technique of 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, and also test our proposed method on surface-electromyogram (abbr. s-EMG) recognition problems with changes in the electrode position.