An operator method for semi-supervised learning

We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general- purpose learner. We proposed a semi-learning algorithm based on a novel form of regularization that allows us to emphasize the complexity of the representation of learners. With operator method, the optimal learner learned by such algorithm is explicitly represented by sampling operator when the hyperspace is a reproducing kernel Hilbert space. Based on such explicit representation, a simple and convenient algorithm is designed. Some preliminary experiments validate the effectiveness of the algorithm.