Speed-Up LOO-CV with SVM classifier

Authors:
G. Lebrun;O. Lezoray;C. Charrier;H. Cardot
Affiliations:
IUT Dépt. SRC, LUSAC EA 2607, groupe Vision et Analyse d’Image, Saint-Lô, France;IUT Dépt. SRC, LUSAC EA 2607, groupe Vision et Analyse d’Image, Saint-Lô, France;IUT Dépt. SRC, LUSAC EA 2607, groupe Vision et Analyse d’Image, Saint-Lô, France;Laboratoire Informatique (EA 2101), Université François-Rabelais de Tours, Tours, France
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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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.