Comparative analysis of interpolative and non-interpolative fuzzy rule based machine learning systems applying various numerical optimization methods

Krisztián Balázs, János Botzheim, László T. Kóczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

In this paper interpolative and non-interpolative fuzzy rule based machine learning systems are investigated by using simulation results. The investigation focuses mainly on two objectives: to compare the efficiency of the inference techniques combined with different numerical optimization methods for solving machine learning problems and to discover the difference between the properties of systems applying interpolative and non-interpolative inference techniques.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010
DOIs
Publication statusPublished - Nov 25 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain
Duration: Jul 18 2010Jul 23 2010

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
CountrySpain
CityBarcelona
Period7/18/107/23/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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    Balázs, K., Botzheim, J., & Kóczy, L. T. (2010). Comparative analysis of interpolative and non-interpolative fuzzy rule based machine learning systems applying various numerical optimization methods. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 [5584156] (2010 IEEE World Congress on Computational Intelligence, WCCI 2010). https://doi.org/10.1109/FUZZY.2010.5584156