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• W2048910220 abstract "Selecting the optimal parameters for support vector machine (SVM) has long been a hot research topic. Aiming for support vector classification/regression (SVC/SVR) with the radial basis function (RBF) kernel, we summarize the rough line rule of the penalty parameter and kernel width, and propose a novel linear search method to obtain these two optimal parameters. We use a direct-setting method with thresholds to set the epsilon parameter of SVR. The proposed method directly locates the right search field, which greatly saves computing time and achieves a stable, high accuracy. The method is more competitive for both SVC and SVR. It is easy to use and feasible for a new data set without any adjustments, since it requires no parameters to set." @default.
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• W2048910220 date "2011-11-01" @default.
• W2048910220 modified "2023-09-27" @default.
• W2048910220 title "Novel linear search for support vector machine parameter selection" @default.
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• W2048910220 doi "https://doi.org/10.1631/jzus.c1100006" @default.
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