An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines

Cheng-Hsuan Li*, Chin-Teng Lin, Bor-Chen Kuo, Hsin-Hua Ho

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

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 SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.
Original languageEnglish
Title of host publicationINTERNATIONAL CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI 2010)
PublisherIEEE
Pages226-232
Number of pages7
ISBN (Print)9780769542539
DOIs
StatePublished - Nov 2010
Event15th Annual International Conference on Technologies and Applications of Artificial Intelligence (TAAI) - Hsinchu, Taiwan
Duration: 18 Sep 201020 Sep 2010

Publication series

NameConference on Technologies and Applications of Artificial Intelligence
PublisherIEEE
ISSN (Print)2376-6816

Conference

Conference15th Annual International Conference on Technologies and Applications of Artificial Intelligence (TAAI)
CountryTaiwan
CityHsinchu
Period18/09/1020/09/10

Keywords

  • support vector machine; SVM; kernel method; optimal kernel; normalized kernel

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