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Conference Paper

Trading Convexity for Scalability

MPS-Authors
/persons/resource/persons84226

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

Fulltext (public)

ICML-2006-Collobert.pdf
(Any fulltext), 290KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Trading Convexity for Scalability. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 201-208). New York, NY, USA: ACM Press.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D141-E
Abstract
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how non-convexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.