Header logo is

Semi-Supervised Classification by Low Density Separation

2005

Conference Paper

ei


We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing the Transductive SVM objective function, which places the decision boundary in low density regions, by gradient descent; 3. combining the first two to make maximum use of the cluster assumption. We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.

Author(s): Chapelle, O. and Zien, A.
Book Title: AISTATS 2005
Journal: Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)
Pages: 57-64
Year: 2005
Month: January
Day: 0
Editors: Cowell, R. , Z. Ghahramani

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Tenth International Workshop on Artificial Intelligence and Statistics (AI & Statistics 2005)
Event Place: Barbados

Digital: 0
ISBN: 0-9727358-1-X
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{2899,
  title = {Semi-Supervised Classification by Low Density Separation},
  author = {Chapelle, O. and Zien, A.},
  journal = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)},
  booktitle = {AISTATS 2005},
  pages = {57-64},
  editors = {Cowell, R. , Z. Ghahramani},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2005},
  month_numeric = {1}
}