Semi-Supervised Classification by Low Density Separation

Chapelle, O; Zien, A

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Semi-Supervised Classification by Low Density Separation

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D697-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-3A84-5

Genre: Conference Paper

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http://www.gatsby.ucl.ac.uk/aistats/aistats2005_eproc.pdf (Any fulltext) Open Access status unknown

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Creators:
Chapelle, O^{1, 2}, Author
Zien, A^{1, 2}, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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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.

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Dates: Date issued: 2005-01

Publication Status: Issued

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Identifiers: BibTex Citekey: 2899

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Title: Tenth International Workshop on Artificial Intelligence and Statistics (AI Statistics 2005)

Place of Event: Barbados

Start-/End Date: 2005-01-06 - 2005-01-08

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Title: AISTATS 2005: Tenth International Workshop onArtificial Intelligence and Statistics

Source Genre: Proceedings

Creator(s):
Cowell, R, Editor
Ghahramani, Z, Editor

Affiliations:
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Publ. Info: The Society for Artificial Intelligence and Statistics

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 57 - 64 Identifier: ISBN: 0-9727358-1-X