General L1-norm Support Vector Machine For Feature Selection
It has been shown in that the traditional L1-norm SVM proposed by Bradley and Mangasarian in can be generalized to a general L1-norm SVM (GL1-SVM).
Moreover, it has been proved that solving the new proposed optimization problem (GL1-SVM) gives smaller error penalty and enlarges the margin between two support vector hyper-planes, thus possibly gives better generalization capability of SVM than solving the traditional L1-norm SVM.
GL1-SVM may also be seen a special case of some generic feature selectors.
Read more about this topic: Feature Selection
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