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  Training and Approximation of a Primal Multiclass Support Vector Machine

Zien, A., Bona, F., & Ong, C. (2007). Training and Approximation of a Primal Multiclass Support Vector Machine. In C. Skiadas (Ed.), XIIth International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007) (pp. 1-8).

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 Urheber:
Zien, A1, 2, 3, Autor           
Bona, FD, Autor
Ong, CS1, 2, 3, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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Schlagwörter: -
 Zusammenfassung: We revisit the multiclass support vector machine (SVM) and generalize
the formulation to convex loss functions and joint feature maps. Motivated by
recent work [Chapelle, 2006] we use logistic loss and softmax to enable gradient
based primal optimization. Kernels are incorporated via kernel principal component
analysis (KPCA), which naturally leads to approximation methods for large scale
problems. We investigate similarities and differences to previous multiclass SVM
approaches. Experimental comparisons to previous approaches and to the popular
one-vs-rest SVM are presented on several different datasets.

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 Datum: 2007-06
 Publikationsstatus: Erschienen
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 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 3983
 Art des Abschluß: -

Veranstaltung

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Titel: XIIth International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007)
Veranstaltungsort: Chania, Greece
Start-/Enddatum: 2007-05-29 - 2007-06-01

Entscheidung

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Titel: XIIth International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007)
Genre der Quelle: Konferenzband
 Urheber:
Skiadas, CH, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1 - 8 Identifikator: -