  We have developed a method for fast and accurate stellar population
parameters determination in order to apply it to high resolution galaxy
spectra. The method is based on an optimization technique that combines active
learning with an instance-based machine learning algorithm. We tested the
method with the retrieval of the star-formation history and dust content in
"synthetic" galaxies with a wide range of S/N ratios. The "synthetic" galaxies
where constructed using two different grids of high resolution theoretical
population synthesis models. The results of our controlled experiment shows
that our method can estimate with good speed and accuracy the parameters of the
stellar populations that make up the galaxy even for very low S/N input. For a
spectrum with S/N=5 the typical average deviation between the input and fitted
spectrum is less than 10**{-5}. Additional improvements are achieved using
prior knowledge.
