Support Vector Machines in Fuzzy Regression

  • Paulina Wieszczy
  • Przemysław GrzegorzewskiEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 634)


This paper presents methods of estimating fuzzy regression models based on support vector machines. Starting from the approaches known from the literature and dedicated to triangular fuzzy numbers and based on linear and quadratic loss, a new method applying loss function based on the Trutschnig distance is proposed. Furthermore, a generalization of those models for fuzzy numbers with trapezoidal membership function is given. Finally, the proposed models are illustrated and compared in the examples and some of their properties are discussed.


Fuzzy numbers Fuzzy regression Loss function Support vector machines 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  2. 2.Department of Gastroenterology, Hepatology and Clinical Oncology, Medical Center for Postgraduate EducationWarsawPoland
  3. 3.Systems Research Institute, Polish Academy of SciencesWarsawPoland

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