1. Speed-Up LOO-CV with SVM Classifier.
- Author
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Corchado, Emilio, Yin, Hujun, Botti, Vicente, Fyfe, Colin, Lebrun, G., Lezoray, O., Charrier, C., and Cardot, H.
- Abstract
Leave-one-out Cross Validation (LOO-CV) gives an almost unbiased estimate of the expected generalization error. But the LOO-CV classical procedure with Support Vector Machines (SVM) is very expensive and cannot be applied when training set has more that few hundred examples. We propose a new LOO-CV method which uses modified initialization of Sequential Minimal Optimization (SMO) algorithm for SVM to speed-up LOO-CV. Moreover, when SMO's stopping criterion is changed with our adaptive method, experimental results show that speed-up of LOO-CV is greatly increased while LOO error estimation is very close to exact LOO error estimation. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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