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Nonlinear Extension of the IRLS Classifier Using Clustering with Pairs of Prototypes

  • Michal JezewskiEmail author
  • Jacek M. Leski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)

Abstract

Classification is the primal problem in pattern recognition. The quadratic loss function is not good approximation of the misclassification error. In the presented paper a nonlinear extension of the IRLS classifier, which uses different loss functions, is proposed. The extension is done by means of fuzzy if-then rules. The fuzzy clustering with pairs of prototypes is applied to establish rules parameters values. Classification quality and computing time obtained for six benchmark databases is compared with the Lagrangian SVM method.

Keywords

Fuzzy Clustering Rule-Base Classifiers IRLS 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland
  2. 2.Institute of Medical Technology and EquipmentSilesian University of TechnologyZabrzePoland

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