Kai Yu, Volker Tresp and Dengyong Zhou
Department of Empirical Inference, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
Considerable progress was recently achieved on semi-supervised learning, which di ers from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that e ectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows ecient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.
|EPrint Type:||Monograph (Technical Report)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Dengyong Zhou|
|Deposited On:||24 December 2004|