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Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers

Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers,10.1145/1390681.1390698,Journal of Machine Learning Res

Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers   (Citations: 5)
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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 accu- racy. 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 nor- mality 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.
Journal: Journal of Machine Learning Research - JMLR , vol. 9, pp. 521-540, 2008
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