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Trading Convexity for Scalability

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. (2007). Trading Convexity for Scalability. In Large Scale Kernel Machines (pp. 275-300). Cambridge, MA, USA: MIT Press.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CC09-2
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 nonconvexity 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.