Semi-supervised Classification by Probabilistic Relaxation

In this paper, a semi-supervised approach based on probabilistic relaxation theory is presented. It combines two desirable properties; firstly, a very small number of labelled samples is needed and, secondly, the assignment of labels is consistently performed according to our contextual information constraints. The proposed technique has been successfully applied to pattern recognition problems, obtaining promising preliminary results in database classification and image segmentation. Our methodology has also been evaluated against a recent state-of-the-art algorithm for semi-supervised learning, obtaining generally comparable or better results.