Abstract: Classical classification algorithm of SVM via p norm regularization usually takes the regularization parameter p as 0,1 or 2.However,large amount of experiments show that these parameters can not always achieve the best classification results.It means finding out the appropriate parameter according to specific dataset may help promote the predictive rate.LIU Jian-wei has already discussed this problem.However,as it is based on the idea of reweighed iteration,it only gets the approximate solution of the original problem.The original problem was solved from the point of optimization when 0＜p＜1.Three different kinds of SVM have been discussed and the classification results are shown with the experiments on three gene datasets.

Key words: p-norm, support vector machine, conjugate gradient algorithm