Semi-supervised SVM classification method based on cluster kernel
一种基于聚类核的半监督支持向量机分类方法

In order to improve the classification accuracy of support vector machine when limited the labeled samples, this paper proposed a semi-supervised support vector machine classification method. It constructed the kernel function according to the cluster assumption. The method used both the labeled and the unlabeled samples to construct the kernel function by the K-means algorithm. The similarity between the samples could be represented better by such kernel function. Then it used in SVM to train and obtain the classification results. Theoretical analysis and computer simulation results show that the algorithm can effectively use a large number of unlabeled samples, and can improve the classification accuracy.