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.
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This work was presented in part at the 16th International Symposium on Artificial Life and Robotics, Oita, Japan, January 27–29, 2011
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Tamura, H., Yoshimatsu, T., Yamashita, S. et al. A study of SVM using a combination of the online learning method and the midpoint-validation method. Artif Life Robotics 16, 283–287 (2011). https://doi.org/10.1007/s10015-011-0912-1
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DOI: https://doi.org/10.1007/s10015-011-0912-1