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Most of machine learning problems assume, that we have at our disposal objects originating from two or more classes. By learning from a representative training set a classifier is able to estimate proper decision boundaries. However, in many real-life problems obtaining objects from some of the classes is difficult, or even impossible. In such cases, we are dealing with oneclass classification, or learning in the absence of counterexamples. Such recognition systems must display a highdoi:10.1016/j.procs.2015.05.351 fatcat:uvdzwt2nfrdltf4tk5gctfd6g4