Semi-Supervised Learning with the help of Parzen Windows

Semi-Supervised Learning is a family of machine learning techniques that make use of both labeled and unlabeled data for training, typically a small amount of labeled data with a large number of unlabeled data. In this paper we propose a Semi-Supervised regression algorithm by means of density estimator, generated by Parzen Windows functions under the framework of Semi-Supervised Learning. We conduct error analysis by capacity independent technique and obtain some satisfactory learning rates in terms of regularity of the target function and the decay condition on the marginal distribution near the boundary.