Semi-Supervised Induction
2004
Technical Report
ei
Considerable progress was recently achieved on semi-supervised learning, which differs 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 effectively 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 effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.
Author(s): | Yu, K. and Tresp, V. and Zhou, D. |
Number (issue): | 141 |
Year: | 2004 |
Month: | August |
Day: | 0 |
Department(s): | Empirische Inferenz |
Bibtex Type: | Technical Report (techreport) |
Institution: | Max Planck Institute for Biological Cybernetics, Tuebingen, Germany |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PDF
|
BibTex @techreport{2782, title = {Semi-Supervised Induction}, author = {Yu, K. and Tresp, V. and Zhou, D.}, number = {141}, organization = {Max-Planck-Gesellschaft}, institution = {Max Planck Institute for Biological Cybernetics, Tuebingen, Germany}, school = {Biologische Kybernetik}, month = aug, year = {2004}, doi = {}, month_numeric = {8} } |