PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-supervised Induction
Kai Yu, Volker Tresp and Dengyong Zhou
(2004) Technical Report. Department of Empirical Inference, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.

Abstract

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:494
Deposited By:Dengyong Zhou
Deposited On:24 December 2004