Learning algorithms at the service of WISE survey
  We have undertaken a dedicated program of automatic source classification in
the WISE database merged with SuperCOSMOS scans, comprehensively identifying
galaxies, quasars and stars on most of the unconfused sky. We use the Support
Vector Machines classifier for that purpose, trained on SDSS spectroscopic
data. The classification has been applied to a photometric dataset based on
all-sky WISE 3.4 and 4.6 $\mu$m information cross-matched with SuperCOSMOS B
and R bands, which provides a reliable sample of $\sim170$ million sources,
including galaxies at $z_{\rm med}\sim0.2$, as well as quasars and stars. The
results of our classification method show very high purity and completeness
(more than 96\%) of the separated sources, and the resultant catalogs can be
used for sophisticated analyses, such as generating all-sky photometric
redshifts.
