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Training Support Vector Machines with Multiple Equality Constraints

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84012

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

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Zitation

Kienzle, W., & Schölkopf, B. (2005). Training Support Vector Machines with Multiple Equality Constraints. Machine Learning: ECML 2005, 182-193.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D3A1-4
Zusammenfassung
In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.