AN AUTOMATIC METHOD FOR SELECTING THE PARAMETER OF THE RBF KERNEL FUNCTION TO SUPPORT VECTOR MACHINES

Cheng-Hsuan Li, Chin-Teng Lin, Bor-Chen Kuo, Hui-Shan Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 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 RBF 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 publication2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
PublisherIEEE
Pages836-839
Number of pages4
ISBN (Print)978-1-4244-9566-5
DOIs
StatePublished - 2010
Event30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action - Honolulu, United States
Duration: 25 Jun 201030 Jun 2010

Publication series

NameIEEE International Symposium on Geoscience and Remote Sensing IGARSS
PublisherIEEE
ISSN (Print)2153-6996

Conference

Conference30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action
CountryUnited States
CityHonolulu
Period25/06/1030/06/10

Keywords

  • Support vector machine; kernel method; optimal kernel

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    Li, C-H., Lin, C-T., Kuo, B-C., & Chu, H-S. (2010). AN AUTOMATIC METHOD FOR SELECTING THE PARAMETER OF THE RBF KERNEL FUNCTION TO SUPPORT VECTOR MACHINES. In 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (pp. 836-839). (IEEE International Symposium on Geoscience and Remote Sensing IGARSS). IEEE. https://doi.org/10.1109/IGARSS.2010.5649251