https://scholars.lib.ntu.edu.tw/handle/123456789/115492
標題: | Leave-one-out Bounds for Support Vector Regression Model Selection | 作者: | Chang, Ming-Wei Lin, Chih-Jen |
公開日期: | 2005 | 起(迄)頁: | 1188-1222 | 來源出版物: | Neural Computation | 摘要: | Minimizing bounds of leave-one-out (loo) errors is an important & efficient approach for support vector machine (SVM) model selection. Past research focuses on their use for classification but not regression. In this article, we derive various loo bounds for support vector regression (SVR) & discuss the difference from those for classification. Experiments demonstrate that the proposed bounds are competitive with Bayesian SVR for parameter selection. We also discuss the differentiability of loo bounds. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/20060927122856648470 | 其他識別: | 20060927122856648470 | DOI: | 10.1162/0899766053491869 |
顯示於: | 資訊工程學系 |
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svrbound.pdf | 250.16 kB | Adobe PDF | 檢視/開啟 |
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