Access provided by: anon Sign Out. Sparse representation based classification SRC has been very successful in many pattern recognition problems. This is a preview of subscription content, log in to check access. ENW EndNote. Experiments on a large number of toy and real-world data sets show that the resultant classifier is compact and accurate, and can also be easily trained by simply alternating linear program and standard SVM solver. Theory, Bertinoro, Italy, pp.
This paper presents an approach to build.
Building Sparse Large Margin Classifiers OpenReview
Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the stan- dard Support Vector Machine. Building Sparse Large Margin Classifiers.
Mingrui Wu, Bernhard Schölkopf, Gökhan Bakir. Max Planck Institute for Biological Cybernetics. This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support.
Two formulations of such sparse multiple-kernel classifiers are proposed. Experiments Basing our experiments on recent comparisons between sparse SVM optimizers Keerthi et al.
Building Sparse MultipleKernel SVM Classifiers IEEE Journals & Magazine
Figure 1. Images of XVs for separating '3' and '8' - "Building Sparse Large Margin Classifiers".
Empirically, the second formulation is particularly competitive. The experimental results of face databases indicated recognition performance of new method is superior to other state-of-the-art methods.
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