Searching for previously unknown classes of objects in the AKARI-NEP
  Deep data with fuzzy logic SVM algorithm
  In this proceedings application of a fuzzy Support Vector Machine (FSVM)
learning algorithm, to classify mid-infrared (MIR) sources from the AKARI NEP
Deep field into three classes: stars, galaxies and AGNs, is presented. FSVM is
an improved version of the classical SVM algorithm, incorporating measurement
errors into the classification process; this is the first successful
application of this algorithm in the astronomy. We created reliable catalogues
of galaxies, stars and AGNs consisting of objects with MIR measurements, some
of them with no optical counterparts. Some examples of identified objects are
shown, among them O-rich and C-rich AGB stars.
