The research of parameters automatic selection reduction Tikhonov regularized SVM

Tikhonov regularized SVM is a kind of new SVM which can convert multi-class problems to be single optimized problems. Since SVM has some limitations in disposition of big data collection, this paper puts forward a new reduction Tikhonov regularized SVM by utilizing pruning algorithm to gain reduction data collection. Meanwhile, the paper applies genetic algorithm to make automatic selection from the balance parameter and kernel function parameter of Tikhonov regularized SVM. The experiment proves this newly improved Tikhonov Regularized SVM is more advantageous for classifying precision and train rate.