Abstract
We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.
Originalsprog | Engelsk |
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Titel | Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 |
Antal sider | 6 |
Forlag | IEEE |
Publikationsdato | 2010 |
Sider | 352-357 |
Artikelnummer | 5708856 |
ISBN (Trykt) | 978-0-7695-4300-0 |
DOI | |
Status | Udgivet - 2010 |
Udgivet eksternt | Ja |
Begivenhed | 9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, USA Varighed: 12 dec. 2010 → 14 dec. 2010 |
Konference
Konference | 9th International Conference on Machine Learning and Applications, ICMLA 2010 |
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Land/Område | USA |
By | Washington, DC |
Periode | 12/12/2010 → 14/12/2010 |
Sponsor | Association for Machine Learning and Applications, IEEE, California State University Bakersfield, Wayne State University |