title: Semisupervised learning using Bayesian interpretation: application to LS-SVM.
abstract: Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.
authors.affiliation: Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, University of Quebec, Montreal, QC H3C 1K3, Canada. mathias.adankon@synchromedia.ca
authors.forename: Mathias M
authors.initials: MM
authors.lastname: Adankon
authors.forename: Mohamed
authors.initials: M
authors.lastname: Cheriet
authors.forename: Alain
authors.initials: A
authors.lastname: Biem
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D016018:Least-Squares Analysis; D016011:Normal Distribution; D010363:Pattern Recognition, Automated