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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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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D131-1 Version Permalink: https://hdl.handle.net/21.11116/0000-0004-9A88-5
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1143844.1143868 (Publisher version)
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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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 Dates: 2006-06
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1145/1143844.1143868
BibTex Citekey: 3931
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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