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

研究成果: Conference contribution同行評審

35 引文 斯高帕斯(Scopus)

摘要

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.
原文American English
主出版物標題2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
發行者IEEE
頁面836-839
頁數4
ISBN(列印)978-1-4244-9566-5
DOIs
出版狀態Published - 七月 2010
事件30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action - Honolulu, United States
持續時間: 25 六月 201030 六月 2010

出版系列

名字IEEE International Symposium on Geoscience and Remote Sensing IGARSS
發行者IEEE
ISSN(列印)2153-6996

Conference

Conference30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action
國家United States
城市Honolulu
期間25/06/1030/06/10

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