Title :
Help-training semi-supervised LS-SVM
Author :
Adankon, Mathias M. ; Cheriet, Mohamed
Author_Institution :
Ecole de Technol. Super., Univ. of Quebec, Montreal, QC, Canada
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 (LS-SVM) 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 semisupervised methods.
Keywords :
learning (artificial intelligence); least squares approximations; pattern classification; support vector machines; discriminative classifier; generative classifier; help-training semi supervised LS-SVM; least squares support vector machine; unlabeled data; Humans; Labeling; Least squares methods; Neural networks; Pattern recognition; Quadratic programming; Semisupervised learning; Supervised learning; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178732
