Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
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Semi-supervised learning has become an active area of recent research in machine learning. To date, many approaches to semi-supervised learning are presented. In this paper, Consistency method and its some variants are deeply studied. The proof about the important condition for convergence of consistency method is given in detail. Moreover, we further study the validity of some variants of consistency method. Finally we conduct the experimental study on the parameters involved in consistency method to face recognition. Meanwhile, the performance of Consistency method and its some variants are compared with that of support vector machine supervised learning methods.