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http://dx.doi.org/10.7465/jkdi.2014.25.2.455

A transductive least squares support vector machine with the difference convex algorithm  

Shim, Jooyong (Department of Data Science, Inje University)
Seok, Kyungha (Department of Data Science, Inje University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.2, 2014 , pp. 455-464 More about this Journal
Abstract
Unlabeled examples are easier and less expensive to obtain than labeled examples. Semisupervised approaches are used to utilize such examples in an eort to boost the predictive performance. This paper proposes a novel semisupervised classication method named transductive least squares support vector machine (TLS-SVM), which is based on the least squares support vector machine. The proposed method utilizes the dierence convex algorithm to derive nonconvex minimization solutions for the TLS-SVM. A generalized cross validation method is also developed to choose the hyperparameters that aect the performance of the TLS-SVM. The experimental results conrm the successful performance of the proposed TLS-SVM.
Keywords
Difference convex algorithm; generalized cross validation function; kernel trick; least squares support vector machine; semisupervised learning; transductive least squares support vector machine;
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Times Cited By KSCI : 2  (Citation Analysis)
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