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	Estimating the confidence interval for prediction errors of support vector machine classifiers
	Jiang, Bo ; Zhang, Xuegong ; Cai, Tianxi
	2010-05-06 ; 2010-05-06
关键词	k-fold cross-validation model evaluation perturbation-resampling prediction errors support vector machine CROSS-VALIDATION RESAMPLING METHODS MODEL SELECTION BOOTSTRAP INDINAVIR ALGORITHMS JACKKNIFE STABILITY PHENOTYPE GENOTYPE Automation & Control Systems Computer Science, Artificial Intelligence
中文摘要	Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluate its prediction accuracy. In this paper, we develop procedures for obtaining both point and interval estimators for the prediction error. Under mild regularity conditions, we derive the consistency and asymptotic normality of the prediction error estimators for SVM with finite-dimensional kernels. A perturbation-resampling procedure is proposed to obtain interval estimates for the prediction error in practice. With numerical studies on simulated data and a benchmark repository, we recommend the use of interval estimates centered at the cross-validated point estimates for the prediction error. Further applications of the proposed procedure in model evaluation and feature selection are illustrated with two examples.
语种	英语 ; 英语
出版者	MICROTOME PUBL ; BROOKLINE ; 31 GIBBS ST, BROOKLINE, MA 02446 USA
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9412]
专题	清华大学
推荐引用方式 GB/T 7714	Jiang, Bo,Zhang, Xuegong,Cai, Tianxi. Estimating the confidence interval for prediction errors of support vector machine classifiers[J],2010, 2010.
APA	Jiang, Bo,Zhang, Xuegong,&Cai, Tianxi.(2010).Estimating the confidence interval for prediction errors of support vector machine classifiers..
MLA	Jiang, Bo,et al."Estimating the confidence interval for prediction errors of support vector machine classifiers".(2010).

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