Help-training semi-supervised LS-SVM

Authors:
Mathias M. Adankon;Mohamed Cheriet
Affiliations:
Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, University of Quebec, Montreal, Canada;Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, University of Quebec, Montreal, Canada
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 16
Cited 0

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Abstract

Help-training for semi-supervised learning was proposed in our previous work in order to reinforce self-training strategy by using a generative classifier along with the main discriminative classifier. This paper extends the Help-training method to least squares support vector machine (LSSVM) where labeled and unlabeled data are used for training. Experimental results on both artificial and real problems show its usefulness when comparing with other classical semi-supervised methods.