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Journal Article

Large Scale Transductive SVMs

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84226

Sinz,  F
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Large Scale Transductive SVMs. Journal of Machine Learning Research, 7, 1687-1712. Retrieved from http://jmlr.csail.mit.edu/papers/volume7/collobert06a/collobert06a.pdf.


Cite as: http://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.