Title :
Comparative analysis of interpolative and non-interpolative fuzzy rule based machine learning systems applying various numerical optimization methods
Author :
Balázs, Krisztián ; Botzheim, János ; Kóczy, László T.
Author_Institution :
Dept. of Telecommun. & Media Inf., Budapest Univ. of Technol. & Econ., Budapest, Hungary
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.
Keywords :
evolutionary computation; fuzzy set theory; inference mechanisms; interpolation; learning (artificial intelligence); inference techniques; interpolative fuzzy rule; machine learning systems; noninterpolative fuzzy rule; numerical optimization methods; Genetics; Machine learning; Memetics; Microorganisms; Optimization; Particle swarm optimization;
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6919-2
DOI :
10.1109/FUZZY.2010.5584156
