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

doi:10.1109/IGARSS.2010.5649251

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 proceeding › Conference contribution › peer-review

35 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 language	English
Title of host publication	2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Publisher	IEEE
Pages	836-839
Number of pages	4
ISBN (Print)	978-1-4244-9566-5
DOIs	https://doi.org/10.1109/IGARSS.2010.5649251
State	Published - 2010
Event	30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action - Honolulu, United States Duration: 25 Jun 2010 → 30 Jun 2010

Publication series

Name	IEEE International Symposium on Geoscience and Remote Sensing IGARSS
Publisher	IEEE
ISSN (Print)	2153-6996

Conference

Conference	30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action
Country	United States
City	Honolulu
Period	25/06/10 → 30/06/10

Keywords

Support vector machine; kernel method; optimal kernel

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@inproceedings{4d95b83f81bd4560a901de5f0ec90920,

title = "AN AUTOMATIC METHOD FOR SELECTING THE PARAMETER OF THE RBF KERNEL FUNCTION TO SUPPORT VECTOR MACHINES",

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.",

keywords = "Support vector machine; kernel method; optimal kernel",

author = "Cheng-Hsuan Li and Chin-Teng Lin and Bor-Chen Kuo and Hui-Shan Chu",

year = "2010",

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language = "English",

isbn = "978-1-4244-9566-5",

series = "IEEE International Symposium on Geoscience and Remote Sensing IGARSS",

publisher = "IEEE",

pages = "836--839",

booktitle = "2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM",

note = "null ; Conference date: 25-06-2010 Through 30-06-2010",

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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. IEEE International Symposium on Geoscience and Remote Sensing IGARSS, IEEE, pp. 836-839, 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action, Honolulu, United States, 25/06/10. https://doi.org/10.1109/IGARSS.2010.5649251

AN AUTOMATIC METHOD FOR SELECTING THE PARAMETER OF THE RBF KERNEL FUNCTION TO SUPPORT VECTOR MACHINES. / Li, Cheng-Hsuan; Lin, Chin-Teng; Kuo, Bor-Chen; Chu, Hui-Shan.

2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM. IEEE, 2010. p. 836-839 (IEEE International Symposium on Geoscience and Remote Sensing IGARSS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lin, Chin-Teng

AU - Kuo, Bor-Chen

AU - Chu, Hui-Shan

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N2 - 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.

AB - 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.

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