Training and Approximation of a Primal Multiclass Support Vector Machine

Zien, A; Bona, FD; Ong, CS

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Training and Approximation of a Primal Multiclass Support Vector Machine

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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;
Friedrich Miescher Laboratory, Max Planck Society;

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Ong, CS
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Friedrich Miescher Laboratory, Max Planck Society;

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http://www.asmda.com//CDasmda2007a/papers/papers_uvwxyz/ZIEN_Training_Approximation_Primal_Multiclass_ASMDA2007_paper.pdf
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Citation

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).

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CD6D-7

Abstract

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.