KERNEL HO-KASHYAP CLASSIFIER WITH GENERALIZATION CONTROL

This paper introduces a new classifier design method based on a kernel extension of the classical Ho-Kashyap procedure. The proposed method uses an approximation of the absolute error rather than the squared error to design a classifier, which leads to robustness against outliers and a better approximation of the misclassification error. Additionally, easy control of the generalization ability is obtained using the structural risk minimization induction principle from statistical learning theory. Finally, examples are given to demonstrate the validity of the introduced method. Keywords: kernel methods, classifier design, Ho-Kashyap classifier, generalization control, robust methods 1

A FUZZY SYSTEM WITH �-INSENSITIVE LEARNING OF PREMISES AND CONSEQUENCES OF IF–THEN RULES

First, a fuzzy system based on if-then rules and with parametric consequences is recalled. Then, it is shown that the global and local �-insensitive learning of the above fuzzy system may be presented as a combination of both an �-insensitive gradient method and solving a system of linear inequalities. Examples are given of using the introduced method to design fuzzy models of real-life data. Simulation results show an improvement in the generalization ability of a fuzzy system trained by the new method compared with the traditional and other �-insensitive learning methods