Astronomy and Astrophysics – Astrophysics
Scientific paper
2003-11-28
Astronomy and Astrophysics
Astrophysics
4 pages, 1 figure, To appear in Proceedings of the ADASS-XIII Conference in Strasbourg, October 2003
Scientific paper
We present a new technique to segregate old and young stellar populations in galactic spectra using machine learning methods. We used an ensemble of classifiers, each classifier in the ensemble specializes in young or old populations and was trained with locally weighted regression and tested using ten-fold cross-validation. Since the relevant information concentrates in certain regions of the spectra we used the method of sequential floating backward selection offline for feature selection. The application to Seyfert galaxies proved that this technique is very insensitive to the dilution by the Active Galactic Nucleus (AGN) continuum. Comparing with exhaustive search we concluded that both methods are similar in terms of accuracy but the machine learning method is faster by about two orders of magnitude.
Estrada-Piedra Trilce
Fuentes Olac
Terlevich Elena
Terlevich Roberto
Torres-Papaqui Juan Pablo
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