Support vector machines in fuzzy regression

Paulina Wieszczy , Przemysław Grzegorzewski

Abstract

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.

Author Paulina Wieszczy - [Warsaw University of Technology (PW)]
Paulina Wieszczy,,
-
- Politechnika Warszawska
, Przemysław Grzegorzewski (FMIS / DIE)
Przemysław Grzegorzewski,,
- Department of Integral Equations
Pages103-138
Book Studies in Computational Intelligence, Studies in Computational Intelligence, 2016, ISBN 9783319301648, 103-138 p.
ASJC Classification1702 Artificial Intelligence
DOIDOI:10.1007/978-3-319-30165-5_6
Languageen angielski
Score (nominal)0
ScoreMinisterial score = 0.0, 06-12-2019, MonographChapterAuthor
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.39
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