In this article, we compare the performance of a new kernel machine with respect to support vector machines (SVM) for prediction of the subnuclear localization of a protein from the primary sequence information. Both machines use the same type of kernel but differ in the criteria to build the classifier. To measure the similarity between protein sequences we employ a
-spectrum kernel to exploit the contextual information around an amino acid and the conserved motif information. We choose Nuc-PLoc benchmark datasets to evaluate both methods. In most subnuclear locations our classifier has better overall accuracy than SVM. Moreover, our method shows less computational cost than SVM.