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ID 19614
Eprint ID
19614
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
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.
キーワード
Orthogonal least-squares
over-fitting
gradient descent
learning rules
error reduction ratio
mean square error
発行日
2009-11-10
出版物タイトル
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
2009巻
1号
出版者
IEEE SMC Hiroshima Chapter
開始ページ
18
終了ページ
23
ISSN
1883-3977
NCID
BB00577064
資料タイプ
会議発表論文
言語
English
著作権者
IEEE SMC Hiroshima Chapter
イベント
5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
イベント地
東広島市
イベント地の別言語
Higashi-Hiroshima City
論文のバージョン
publisher
査読
有り
Eprints Journal Name
IWCIA