Abstract: Semi-supervised learning is a hot research topic of machine learning. Co-training is a multi-view semi-superwised learning method. Co-training is studied from the regularization point of view. By exploiting the metric structure of the hypotheses space, the smoothness and consistency of a hypothesis were defined. A two levels regularization algorithm was presented which uses the smoothness to regularize the within-view learning process and uses the consistency to regularize the between-view learning process. The experimental results were presented on both synthetic and real world datascts.

Key words: Artificial intelligence, Machine learning, Semi-supervised learning, Multi-view learning, Regularization