Support Vector Selection for Regression Machines
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
18-23 頁
2009-11 発行
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この文献の参照には次のURLをご利用ください : https://ir.lib.hiroshima-u.ac.jp/00028412
ファイル情報(添付) | |
タイトル ( eng ) |
Support Vector Selection for Regression Machines
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作成者 |
Lee Wan-Jui
Yang Chih-Cheng
Lee Shie-Jue
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収録物名 |
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
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開始ページ | 18 |
終了ページ | 23 |
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 leastsquares 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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著者キーワード |
Orthogonal least-squares
over-fitting, gradient descent
learning rules
error reduction ratio
mean square error
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NDC分類 |
技術・工学 [ 500 ]
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言語 |
英語
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資源タイプ | 会議発表論文 |
出版者 |
IEEE SMC Hiroshima Chapter
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発行日 | 2009-11 |
権利情報 |
(c) Copyright by IEEE SMC Hiroshima Chapter.
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出版タイプ | Version of Record(出版社版。早期公開を含む) |
アクセス権 | オープンアクセス |
収録物識別子 |
[ISSN] 1883-3977
[URI] http://www.hil.hiroshima-u.ac.jp/iwcia/2009/
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