In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...