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Help-Training for semi-supervised support vector machines
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DOI：10.1016/j.patcog.2011.02.015.png)
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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. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Classification
Semi-supervised learning
SVM
Kernel machine
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