Large Scale Transductive SVMs

Collobert, R; Sinz, F; Weston, J; Bottou, L

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Large Scale Transductive SVMs

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Sinz, F
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://jmlr.csail.mit.edu/papers/volume7/collobert06a/collobert06a.pdf
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Citation

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Large Scale Transductive SVMs. The Journal of Machine Learning Research, 7, 1687-1712.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D08D-E

Abstract

We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving
a combinatorial search problem. This
provides for the first time a highly scalable algorithm in the nonlinear case.
Detailed experiments verify the utility of our approach.