Help-Training for semi-supervised support vector machines

In this paper, we propose to reinforce the Self-Training strategy in semi-supervised mode by using a generative classifier that may help to train the main discriminative classifier to label the unlabeled data. We call this semi-supervised strategy Help-Training and apply it to training kernel machine classifiers as support vector machines (SVMs) and as least squares support vector machines. In addition, we propose a model selection strategy for semi-supervised training. Experimental results on both artificial and real problems demonstrate that Help-Training outperforms significantly the standard Self-Training. Moreover, compared to other semi-supervised methods developed for SVMs, our Help-Training strategy often gives the lowest error rate.

► We propose a new semi-supervised learning strategy, called help-training.
► We apply help-training to semi-supervised SVM /LS-SVM.
► Experiments show that help-training outperforms significantly self-training.
► Compared to other methods, help-training often gives the lowest error rate.