A projection neural network for training support vector machines

In this paper, we develop a projection neural network to solve the convex quadratic programming problem in support vector machine (SVM) learning. Then, we obtain a unique global solution for the proposed neural network. Furthermore, we prove that this network is completely stable and finite-time convergence. To present the feasibility and efficiency of the proposed neural network for solving the SVM learning problem, we provide several illustrative examples at the end.