Trading Convexity for Scalability

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

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Trading Convexity for Scalability

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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;

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https://ieeexplore.ieee.org/document/6279960
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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: https://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.