Parameter Selection for Sub-hyper-sphere Support Vector Machine

Abstract

Sub-hyper-sphere support vector machines (SVMs) are proposed for solving the classification of the intersections of hyper-spheres when dealing with multi-class classification problem. Since the Gaussian kernel parameter influences the overlap position of the hyper-spheres, the resulting minimum bounding sphere-based classifier must be chosen optimally. This paper presents a new GA-based parameter selection method to get better generalization accuracy. Experimental results show the proposed approach is feasible and efficient.