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Semi-supervised classification with LS-SVM formulation  

Seok, Kyung-Ha (Inje University Department of Data Science)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.3, 2010 , pp. 461-470 More about this Journal
Abstract
Semi supervised classification which is a method using labeled and unlabeled data has considerable attention in recent years. Among various methods the graph based manifold regularization is proved to be an attractive method. Least squares support vector machine is gaining a lot of popularities in analyzing nonlinear data. We propose a semi supervised classification algorithm using the least squares support vector machines. The proposed algorithm is based on the manifold regularization. In this paper we show that the proposed method can use unlabeled data efficiently.
Keywords
Graph based semi supervised classification; Graph Laplacian; least squares support vector machine; manifold regularization; semi supervised classification;
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Times Cited By KSCI : 4  (Citation Analysis)
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