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We have undertaken a dedicated program of automatic source classification inthe WISE database merged with SuperCOSMOS scans, comprehensively identifyinggalaxies, quasars and stars on most of the unconfused sky. We use the SupportVector Machines classifier for that purpose, trained on SDSS spectroscopicdata. The classification has been applied to a photometric dataset based onall-sky WISE 3.4 and 4.6 $\mu$m information cross-matched with SuperCOSMOS Band 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. Theresults of our classification method show very high purity and completeness(more than 96\%) of the separated sources, and the resultant catalogs can beused for sophisticated analyses, such as generating all-sky photometricredshifts.

Learning algorithms at the service of WISE survey