LS-SVM Applied for Photometric Classification of Quasars and Stars

Details LS-SVM Applied for Photometric Classification of Quasars and Stars LS-SVM Applied for Photometric Classification of Quasars and Stars

Jul 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aspc..442..123z&link_type=abstract

Astronomical Data Analysis Software and Systems XX. ASP Conference Proceedings, Vol. 442, proceedings of a Conference held at Se

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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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