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Semi-Supervised Classification by Low Density Separation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

Chapelle,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84331

Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Chapelle, O., & Zien, A. (2005). Semi-Supervised Classification by Low Density Separation. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), 57-64.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D697-2
Zusammenfassung
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.