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  A Continuation Method for Semi-Supervised SVMs

Chapelle, O., Chi, M., & Zien, A. (2006). A Continuation Method for Semi-Supervised SVMs. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 185-192). New York, NY, USA: ACM Press.

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ICML-2006-Chapelle.pdf (Any fulltext), 292KB
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 Creators:
Chapelle, O1, 2, Author              
Chi, M1, 2, Author              
Zien, A1, 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, ou_1497794              

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 Abstract: Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.

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Language(s):
 Dates: 2006-06
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1143844.1143868
BibTex Citekey: 3931
 Degree: -

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Title: 23rd International Conference on Machine Learning
Place of Event: Pittsburgh, PA., USA
Start-/End Date: 2006-06-25 - 2006-06-29

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Title: ICML '06: Proceedings of the 23rd International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Cohen, W, Editor
Moore, A, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 185 - 192 Identifier: ISBN: 1-59593-383-2