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: |
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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}, doi = {}, month_numeric = {1} } |