Some new developments of support vector machine in high dimension
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Abstract
Some selective new developments of support vector machine(SVM), including non-convex penalized SVM, the error bound of L1 norm SVM, and the application of SVM in sufficient dimension reduction are reviewed.The performance of these new methods in high-dimensional SVM is demonstrated by numerical simulation and real data analysis.Several possible new directions and issues are discussed.
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