Multiclass Support Vector Machines with SCAD
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Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine (SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation (SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the L1, L2 penalty functions and the developed method.
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