Optimization of SVM Method with RBF kernel

被引：6

作者：

Wang Cheng ^{[1
]}

机构：

[1] Hubei Inst Fine Arts, Dept Common Required Courses, Wuhan 430205, Hubei, Peoples R China

来源：

FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE IV, PTS 1-5 | 2014年 / 496-500卷

关键词：

Support Vector Machine; RBF Kernel; Bilinear Grid Search; Model Selection; Parameters Optimization;

D O I：

10.4028/www.scientific.net/AMM.496-500.2306

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Usually there is no a uniform model to the choice of SVM's kernel function and its parameters for SVM. This paper presents a bilinear grid search method for the purpose of getting the parameter (C, gamma) of SVM with RBF kernel, with the approach of combining grid search with bilinear search. Experiment results show that the proposed bilinear grid search. has combined both the advantage of moderate training quantity by the bilinear search and of high predict accuracy by the grid search.

引用

页码：2306 / 2310

页数：5

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