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.
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Lebrun, G., Lezoray, O., Charrier, C., Cardot, H. (2006). Speed-Up LOO-CV with SVM Classifier. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_13
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DOI: https://doi.org/10.1007/11875581_13
Publisher Name: Springer, Berlin, Heidelberg
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