English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Book Chapter

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

External Ressource
Fulltext (public)

Collobert-Sinz.pdf
(Any fulltext), 290KB

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

Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2007). Trading Convexity for Scalability. In L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), 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.