Abstract
In this paper, we introduce a new classification approach that learns class dependent Gaussian kernels and the belongingness likelihood of the data points with respect to each class. The proposed Support Kernel Classification (SKC) is designed to characterize and discriminate between the data instances from the different classes. It relies on the maximization of the intra-class distances and the minimization of the intraclass distances to learn the optimal Gaussian parameters. In fact, a novel objective function is proposed to model each class using one Gaussian function. The experiments conducted using synthetic datasets demonstrated the effectiveness of the proposed algorithm. Moreover, the results obtained using real datasets proved that the proposed classifier outperforms the relevant state of the art approaches.