Recently, a great deal of work within machine learning has gone into the semi-supervised paradigm, which consists in using unlabeled data samples in the hope that additional information on the distribution of training objects improves classification or regression performance. This talk gives a quick overview of semi-supervised versions of two popular kernel algorithms: support vector machines and Gaussian processes as well as discusses a result for arbitrary RHKSs. For support-vector machines, an interpretation wrt. the VC bound is given, whereas for Gaussian processes a Bayesian justification for semi-superivsed learning is provided. An effort is made to discuss the relative merits and weaknesses of each of the approaches, particularly with respect to computational complexity.