Automatised classification of WISE sources: first results, future
  prospects
  We present the first results of our dedicated programme of automatised
classification of galaxies, stars and quasars in the mid-infrared all-sky data
from the WISE survey. We employ the Support Vector Machines (SVM) algorithm,
which defines a hyperplane separating different classes of sources in a
multidimensional space of arbitrarily chosen parameters. This approach consists
of four general steps: 1) selection of the training sample, 2) selection of the
optimal parameter space, 3) training of the classifier, 4) application to
target data. Here, as the training set, we use sources from a cross-correlation
of the WISE catalogue with the SDSS spectroscopic sample. The performance of
the SVM classifier was tested as a function of size of the training set,
dimension of the parameter space, WISE apparent magnitude and Galactic
extinction. We find that our classifier provides promising results already for
three classification parameters: magnitude, colour and differential aperture
magnitude. Completeness and purity levels as high as 95% are obtained for
quasars, while for galaxies and stars they vary between 80-95% depending on the
magnitude, deteriorating for fainter sources.
