Support Vector Machines (SVMs) for classification - in short SVC - have been shown to be promising classification tools in many real-world problems. How to effectively extend binary SVC to multi-class classification is still an on-going research issue. In this article, instead of solving quadratic programming (QP) in Algorithm K-SVCR and Algorithm v-K-SVCR, a linear programming (LP) problem is introduced in our algorithm. This leads to a new algorithm for multi-class problem, K-class Linear programming Support Vector Classification-Regression(K-LSVCR). Numerical experiments on artificial data sets and benchmark data sets show that the proposed method is almost as efficient asK-SVCR and v-K-SVCR, while considerably faster than them.