DocumentCode :
3564356
Title :
An on-line method for self-organized learning and extraction of fuzzy rules from high dimensional data
Author :
Srinivasa, N. ; Medasani, S. ; Owechko, Y.
Author_Institution :
Hughes Res. Labs., Malibu, CA, USA
Volume :
2
fYear :
2001
Firstpage :
670
Abstract :
In this paper we present an approach that is capable of online learning and automatic generation of a fuzzy expert system for high dimensional classification problems. The novel part of our system is a new online learning rule. Unlike other learning systems, this learning rule makes our system scale robustly with input space dimensions and thus suitable for high dimensional data. The algorithm is also able to extract knowledge in an online fashion in the form of fuzzy rules that are comprehensible, compact, and accurate.
Keywords :
expert systems; fuzzy neural nets; knowledge acquisition; learning (artificial intelligence); online operation; pattern classification; self-organising feature maps; fuzzy expert system; fuzzy rule extraction; high-dimensional classification problems; high-dimensional data; knowledge extraction; online self-organized learning; Clustering algorithms; Data mining; Engines; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Hybrid intelligent systems; Laboratories; Robustness; Subspace constraints;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Print_ISBN :
0-7803-7293-X
Type :
conf
DOI :
10.1109/FUZZ.2001.1009044
Filename :
1009044
Link To Document :
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