Abstract When the label proportion of unlabeled data is far away from that of labeled data, direct supervised support vector machine(SVM) with only labeled data outperforms semi-supervised SVM(S3VM) with unlabeled data. Thus, a shifted label proportion aware S3VM(fairS3VM) is proposed. Specifically, the label mean of unlabeled data is firstly estimated. Then multiple label means corresponding to multiple label proportions are integrated under the worst-case scenario. Experimental results show that the performance guarantee of S3VMs is effectively improved when the label proportion is shifted.
About author:: LI Yuanzhao, born in 1992, master student. His research interests include machine learning and data mining.WANG Shaobo, born in 1990, master student. His research interests include machine learning.LI Yufeng(Corresponding author), born in 1983, Ph.D., assistant professor. His research interests include machine learning and data mining.
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2016, Vol. 29 
