Speed-Up LOO-CV with SVM Classifier

Gilles Lebrun (1) , Olivier Lezoray (1) , Christophe Charrier (1) , Hubert Cardot (2)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours

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.

Domaines: Traitement des images [eess.IV]; Traitement du texte et du document; Vision par ordinateur et reconnaissance de formes [cs.CV]

Gilles Lebrun, Olivier Lezoray, Christophe Charrier, Hubert Cardot. Speed-Up LOO-CV with SVM Classifier. Intelligent Data Engineering and Automated Learning, 2006, Burgos, Spain. pp.108-115. ⟨hal-01026334⟩