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.