Support Vector Selection for Regression Machines
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
Page 18-23
published_at 2009-11
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A1004.pdf
570 KB
種類 :
fulltext
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Title ( eng ) |
Support Vector Selection for Regression Machines
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Creator |
Lee Wan-Jui
Yang Chih-Cheng
Lee Shie-Jue
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Source Title |
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
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Start Page | 18 |
End Page | 23 |
Abstract |
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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Keywords |
Orthogonal least-squares
over-fitting, gradient descent
learning rules
error reduction ratio
mean square error
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NDC |
Technology. Engineering [ 500 ]
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Language |
eng
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Resource Type | conference paper |
Publisher |
IEEE SMC Hiroshima Chapter
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Date of Issued | 2009-11 |
Rights |
(c) Copyright by IEEE SMC Hiroshima Chapter.
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Publish Type | Version of Record |
Access Rights | open access |
Source Identifier |
[ISSN] 1883-3977
[URI] http://www.hil.hiroshima-u.ac.jp/iwcia/2009/
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