Abstract
In this paper, we discuss fast training of a support vector machine for pattern classification by extracting training data around the class boundaries, and then compare performance of the support vector machine with that of a fuzzy classifier with ellipsoidal regions. First, we discuss the architecture and the training method of the support vector machine. Then, we discuss how to extract boundary data from the training data. Next, we summarize the architecture of the fuzzy classifier and discuss a feature extraction method using a two-layer neural network. Finally, we compare performance of the support vector machine with the fuzzy classifier combined with the two-layer neural network for several benchmark data sets and demonstrate the effectiveness of the fast training method.
