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  Optimization Techniques for Semi-Supervised Support Vector Machines

Chapelle, O., Sindhwani, V., & Keerthi, S. (2008). Optimization Techniques for Semi-Supervised Support Vector Machines. Journal of Machine Learning Research, 9, 203-233.

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Chapelle, O1, Author              
Sindhwani, V, Author
Keerthi, SS, Author
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1External Organizations, ou_persistent22              

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 Abstract: Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S3VMs. This paper reviews key ideas in this literature. The performance and behavior of various S3VMs algorithms is studied together, under a common experimental setting.

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 Dates: 2008-02
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 5369
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Title: Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Brookline, MA : Microtome Publishing
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 203 - 233 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020