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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Kizuka T, Masuda T, Kiryu T, Sadoyama T (2006) Practical usage of surface electromyograms. Biomechanism Library (in Japanese)
De Luca CJ, Knaflitz M (1992) Surface electromyography: what’s new? CLUT, Torino
Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20(3):273–297
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2): 121–167
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Ogura N, Watanabe S (1999) Online learning for support vector machine using necessity estimation (in Japanese). Technical Report of IEICE PRMU 99(182):45–52
Ikeda K, Yamasaki T (2007) Incremental support vector machines and their geometrical analyses. Neurocomputing 70:2528–2533
Kohonen T (1996) Self-organizing maps. Springer, Tokyo (translation of: Kohonen T (1995) Self-organizing maps. Springer, Berlin, Heidelberg
Tamura H, Gotoh T, Okumura D, et al (2008) A study on s-EMG pattern recognition using neural networks. 2008 International Symposium on Intelligent Informatics (ISII 2008), Extended Abstracts, Kumamoto, Japan, p 46
Kawano S, Tamura H, Okumura D, et al (2008) Online learning method using support vector machine for surface electromyogram recognition. Intl Symp Artif Life Robotics (AROB2008) (CD-ROM), Oita, Japan, pp 227–230
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009
About this article
Cite this article
Tamura, H., Kawano, S. & Tanno, K. Unsupervised learning method for a support vector machine and its application to surface electromyogram recognition. Artif Life Robotics 14, 362–366 (2009). https://doi.org/10.1007/s10015-009-0682-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-009-0682-1