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Paper: LS-SVM Applied for Photometric Classification of Quasars and Stars
Volume: 442, Astronomical Data Analysis Software and Systems XX (ADASSXX)
Page: 123
Authors: Zhang, Y.; Zhao, Y.; Peng, N.
Abstract: The major drawback of Support Vector Machines (SVM) is their higher computational cost for a quadratic programming (QP) problem. In order to overcome this problem, we propose using Least Squares Support Vector Machines (LS-SVM). LS-SVM's solution is given by a linear system, which makes SVM method more generally simple and applicable. In this paper, LS-SVM is used for classification of quasars and stars from SDSS and UKIDSS photometric databases. The result shows that LS-SVM is highly efficient and powerful especially for large scale problem and has comparable performance with that of SVM.
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