DocumentCode
2304356
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
fYear
2010
fDate
18-23 July 2010
Firstpage
1
Lastpage
8
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location
Barcelona
ISSN
1098-7584
Print_ISBN
978-1-4244-6919-2
Type
conf
DOI
10.1109/FUZZY.2010.5584156
Filename
5584156
Link To Document