A kernel fuzzy classifier with ellipsoidal regions
Kenichi Kaieda, Shigeo Abe · 2004
In this paper, we discuss a kernel version of fuzzy classifiers with ellipsoidal regions to improve generalization ability. First, we map the input space into the implicit feature space induced by a kernel function. Then we generate a fuzzy rules in the feature space and tune the slopes of membership functions successively until there is no improvement in the recognition rate of the training data. We evaluate our method using numeral and hiragana data of vehicle license plates, and blood cell data. Except for the numeral data, the generalization ability is improved against that of the conventional fuzzy classifier with ellipsoidal regions, and is comparable to that of support vector machines.