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- W2041722944 abstract "Recent studies have showed that machine learning techniques are advantageous to statistical models for medicine database classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. Three UCI databases are used to demonstrate the good performance of the SVM- MK." @default.
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- W2041722944 date "2013-02-01" @default.
- W2041722944 modified "2023-09-27" @default.
- W2041722944 title "Data Classification Using Support Vector Machines with Mixture Kernels" @default.
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- W2041722944 doi "https://doi.org/10.4028/www.scientific.net/amr.662.936" @default.
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