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- W1617504014 abstract "Least squares support vector machine (LS-SVM) has been successfully applied in many classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack of sparseness. Combing the primal least squares twin support vector machine (LS-TSVM) and the sparse LS-SVM with L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -norm minimization, a new sparse least squares support vector regression algorithm with L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -norm in primal space(L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -PLSSVR) is proposed in this paper. Experiments on the artificial dataset illustrate that the novel L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -PLSSVR algorithm achieves better sparseness and generalization performance than the SVM and LS-SVM algorithm." @default.
- W1617504014 created "2016-06-24" @default.
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- W1617504014 date "2015-08-01" @default.
- W1617504014 modified "2023-10-14" @default.
- W1617504014 title "Sparse least squares support vector machine with L<inf>0</inf>-norm in primal space" @default.
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- W1617504014 doi "https://doi.org/10.1109/icinfa.2015.7279758" @default.
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