Abstract
Multi-view semi-supervised learning is a hot research topic recently. In this paper, we consider the regularization problem in multi-view semi-supervised learning. A regularization method adaptive to the given data is proposed, which can use unlabeled data to adjust the degree of regularization automatically. This new regularization method comprises two levels of regularization simultaneously. Experimental evidence on real word dataset shows its effectivity.
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Wang, J., Luo, S., Li, Y. (2010). A Multi-view Regularization Method for Semi-supervised Learning. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_57
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DOI: https://doi.org/10.1007/978-3-642-13278-0_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
Online ISBN: 978-3-642-13278-0
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