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A Continuation Method for Semi-Supervised SVMs

MPG-Autoren
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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;

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

Externe Ressourcen

https://dl.acm.org/citation.cfm?doid=1143844.1143868
(Verlagsversion)

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ICML-2006-Chapelle.pdf
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Zitation

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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-D131-1
Zusammenfassung
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.