Article ID Journal Published Year Pages File Type
531247 Pattern Recognition 2011 11 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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