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Conference Paper

Semi-Supervised Classification by Low Density Separation

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

/persons/resource/persons84331

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

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Citation

Chapelle, O., & Zien, A. (2005). Semi-Supervised Classification by Low Density Separation. In R. Cowell, & Z. Ghahramani (Eds.), AISTATS 2005: Tenth International Workshop onArtificial Intelligence and Statistics (pp. 57-64). The Society for Artificial Intelligence and Statistics.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D697-2
Abstract
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.