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Building Sparse Large Margin Classifiers

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84321

Wu,  M
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;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83791

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

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Zitation

Wu, M., Schölkopf, B., & BakIr, G. (2005). Building Sparse Large Margin Classifiers. Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 996-1003.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D4B1-9
Zusammenfassung
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The added constraint explicitly controls the sparseness of the classifier and an approach is provided to solve the formulated problem. When considering the dual of this problem, it can be seen that building an SLMC is equivalent to constructing an SVM with a modified kernel function. Further analysis of this kernel function indicates that the proposed approach essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace different classes of data are linearly well separated. Experimental results over several classification benchmarks show that in most cases the proposed approach outperforms the state-of-art sparse learning algorithms.