  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.
