Linear Support Vector Machine Based on Variational Inequality

In order to decrease computational complexity and increase the speed of computerized implementation algorithm while solving quadratic programming problems, this paper puts forward and presents experimental results for an effective training method of Linear support vector machine based on variational inequality (VILSVM). The method is to transform the convex quadratic programming problem into the solving problem of variational inequality during the training process of linear supporting vector, obtaining the optimal separating hyperplane by means of solving problem of variational inequality. During the solving process, it will not generate high-memory data, so that the training and test speed of supporting vector machine in classification could be increased. The transformation formula and the specific algorithm were given in this paper. VILSVM was applied into the multidimensional iris training samples. The simulation result shows that VILSVM has high generalization ability and can identify accurately test sample. In addition, it has faster rate of convergence than traditional supporting vector machine with 88% time reduction averagely.