The Semi-Supervised SVM Classification Algorithm Based on Two-Stage Learning

A semi-supervised support vector machine(semi-supervised SVM) classification algorithm is proposed based on two-stage learning.A graph-based label propagation algorithm is used to provide initial pseudo labels for the unlabeled samples.Andκ-nearest graph is applied to distinguishing and removing the possible noisy samples.Then the denoised samples are inputted into the support vector machine(SVM) as labeled samples,so that the global information of the whole samples can be utilized by SVM when it is used in the training to improve the classification accuracy.The experiment results show that compared with other semi-supervised learning algorithms,the proposed method improves classification performance and is of higher robustness in the case of fewer labeled training samples.