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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. Retrieved from http://jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pdf.

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Chapelle, O1, Autor           
Sindhwani, V, Autor
Keerthi, SS, Autor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: 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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 Datum: 2008-02
 Publikationsstatus: Erschienen
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 Identifikatoren: URI: http://jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pdf
BibTex Citekey: 5369
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Titel: Journal of Machine Learning Research
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 9 Artikelnummer: - Start- / Endseite: 203 - 233 Identifikator: -