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In this proceedings application of a fuzzy Support Vector Machine (FSVM)learning algorithm, to classify mid-infrared (MIR) sources from the AKARI NEPDeep field into three classes: stars, galaxies and AGNs, is presented. FSVM isan improved version of the classical SVM algorithm, incorporating measurementerrors into the classification process; this is the first successfulapplication of this algorithm in the astronomy. We created reliable cataloguesof galaxies, stars and AGNs consisting of objects with MIR measurements, someof them with no optical counterparts. Some examples of identified objects areshown, among them O-rich and C-rich AGB stars.

Searching for previously unknown classes of objects in the AKARI-NEP Deep data with fuzzy logic SVM algorithm