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- W2051212012 abstract "This paper proposed an efficient model l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</sub> -support vector classification(l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</sub> -SVC) which combines C-SVC and feature selection strategy by introducing the l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</sub> -norm (0 <; p <; 1). Following a lower bound for the absolute value of nonzero entries in every local optimal solution of the model, we investigated the relationship between sparsity of the solution and the choice of the regularization parameter and p-norm. In order to solve the nonconvex problem in l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</sub> -SVC, we investigate an iteratively reweighted l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>q</sub> (q ≥ 1) convex relaxation scheme, in which the weighted problem solved by CCCP, and preliminary numerical experiments show the superior effectiveness of our model for identifying nonzero entries in numerical solutions and feature selection than standard l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> convex relaxation. We also apply it to a real-life credit dataset to prove its efficiency." @default.
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- W2051212012 date "2010-08-01" @default.
- W2051212012 modified "2023-09-24" @default.
- W2051212012 title "l<inf>p</inf>-norm support vector machine with CCCP" @default.
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- W2051212012 doi "https://doi.org/10.1109/fskd.2010.5569345" @default.
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