科研之友
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摘要
This paper presents a new version of support vector machine (SVM) named l (2) - l (p) SVM (0 < p < 1) which introduces the l (p) -norm (0 < p < 1) of the normal vector of the decision plane in the standard linear SVM. To solve the nonconvex optimization problem in our model, an efficient algorithm is proposed using the constrained concave-convex procedure. Experiments with artificial data and real data demonstrate that our method is more effective than some popular methods in selecting relevant features and improving classification accuracy.
