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dc.contributor.authorLi, C.H.en_US
dc.contributor.authorHo, H.H.en_US
dc.contributor.authorLiu, Y.L.en_US
dc.contributor.authorLin, C.T.en_US
dc.contributor.authorKuo, B.C.en_US
dc.contributor.authorTaur, J.S.en_US
dc.description.abstractSoft-margin support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the normalized kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding soft-margin SVMs can obtain more accurate or at least equal performance than the soft-margin SVMs by applying k-fold cross-validation to determine the parameters.en_US
dc.relationJournal of Information Science and Engineeringen_US
dc.relation.ispartofseriesJournal of Information Science and Engineering, Volume 28, Issue 1, Page(s) 1-15.en_US
dc.subjectsoft-margin support vector machineen_US
dc.subjectkernel methoden_US
dc.subjectoptimal kernelen_US
dc.subjectnormalized kernelen_US
dc.subjectk-fold cross-validationen_US
dc.subjecthyperspectral image classificationen_US
dc.subjectfeature spaceen_US
dc.titleAn Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machinesen_US
dc.typeJournal Articlezh_TW
item.fulltextno fulltext-
item.openairetypeJournal Article-
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