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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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