ID | 19614 |
Eprint ID | 19614
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フルテキストURL | |
著者 |
Lee Wan-Jui
National Sun Yat-Sen University
Yang Chih-Cheng
National Sun Yat-Sen University
Lee Shie-Jue
National Sun Yat-Sen University
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抄録 | In this paper, we propose a method to select support vectors to improve the performance of support vector regression machines. First, the orthogonal least-squares
method is adopted to evaluate the support vectors based on their error reduction ratios. By selecting the representative support vectors, we can obtain a simpler model which helps avoid the over-fitting problem. Second, the simplified model is further refined by applying the gradient descent method to tune the parameters of the kernel functions. Learning rules for minimizing the regularized risk functional are derived. Experimental results have shown that our approach can improve effectively the generalization capability of support vector regressors.
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キーワード | Orthogonal least-squares
over-fitting
gradient descent
learning rules
error reduction ratio
mean square error
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発行日 | 2009-11-10
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出版物タイトル |
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
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巻 | 2009巻
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号 | 1号
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出版者 | IEEE SMC Hiroshima Chapter
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開始ページ | 18
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終了ページ | 23
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ISSN | 1883-3977
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NCID | BB00577064
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資料タイプ |
会議発表論文
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言語 |
英語
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著作権者 | IEEE SMC Hiroshima Chapter
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イベント | 5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
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イベント地 | 東広島市
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イベント地の別言語 | Higashi-Hiroshima City
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論文のバージョン | publisher
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査読 |
有り
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Eprints Journal Name | IWCIA
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