Improved SMO algorithm of nonlinear regression support vector machine

In order to solve the regression problems of nonlinear data and nonlinear function, the support vector machine (SVM) sequential minimal optimization (SMO) algorithm was adopted. The original SMO algorithm has deficiencies such as low training speed and instability training results. To accelerate the training process of SMO algorithm and promote training stability of the solution, the SMO algorithm was improved by updating the optimization multipliers method, using double threshold values, caching kernel function outputs, adding stop criterion. Simulation results show that the improved algorithm performs well for regression of nonlinear data and nonlinear function, and it has faster training speed and better training result stability than original SMO algorithm.