Abstract
A new direction in machine learning area has emerged from Vapnik's theory in support vectors machine and its applications on pattern recognition. In this paper, we propose a new SVM kernel family (KMOD) with distinctive properties that allots, better discrimination in the feature space. The experiments that we carry out show its effectiveness on synthetic and large-scale data. We found KMOD behaving better than RBF and Exponential RBF kernels on the two-spiral problem. In addition, a digit recognition task was processed using the proposed kernel. The results show; at least, comparable performances to state of the art kernels.