@inproceedings{2113,
  abstract     = {{In this paper, we sketch an idea for the integration of singleclass support vector machines (SVM) into fuzzy class learning. As result,we  obtain  robust  and  transparent  rule-based  fuzzy  classification  models suitable for online-classification tasks. In particular, the singleclass SVM is employed to extend the applicability of convex fuzzy classifica-tion models to nonconvex datainherent structures. The key point of thisextension  is  the  preservation  of  the  interpretability  for  both,  the  classlearning and the classification process. The feasibility of the approach isdemonstrated in the context of a banknote authentication application.}},
  author       = {{Hempel, Arne-Jens and Hähnel, Holger and Mönks, Uwe and Lohweg, Volker}},
  booktitle    = {{BVAu 2012 - 3. Jahresolloquium "Bildverarbeitung in der Automation" Centrum Industrial IT, Lemgo,}},
  keywords     = {{fuzzy  classification, pattern  recognition, single-class  support vector machine, data mining}},
  publisher    = {{inIT-Institut für industrielle Informationstechnik}},
  title        = {{{SVM-integrated Fuzzy Pattern Classification for Nonconvex Data-Inherent Structures Applied to Banknote Authentication}}},
  year         = {{2012}},
}