Abstract: In order to solve the nonconvex and nonsmooth problem of semi-supervised support vector classification, a polynomial smooth function was introduced in this paper which was used to approach the nonconvex objective function.The introduced polynomial function has a high approximation accuracy in high density regions of samples and poor approximation performance appear in low density regions of samples. The model was solved by the method of conjugate gradient. Experimental results on artificial and real data support that the proposed algorithm can guarantee the accuracy when the percentage of labeled sample is very low and the accuracy is not improved obviously as the number of labeled data increasing. The performance of the proposed classifier is stable.

Key words: Semi supervised learning, Support vector machine(SVM) , Classification