Paper Info
Title
A Novel Fuzzy Likelihood Measure Algorithm
Abstract
Similarity measure (PSM) is a kind of measurement that measure the size of similarity between two patterns, it plays a key role in the analysis and research of pattern recognition, machine learning, clustering analysis. This article will firstly study the current PSM theory, point out its application range; secondly, discuss the axiomatic theory of fuzzy likelihood measure, give the frequently-used algorithms of fuzzy likelihood measure that based on axiomatic theory; finally, advance a new kind of fuzzy likelihood function, then establish the fuzzy likelihood measure(FLM) between two fuzzy sets, so as to describe the similar degree between two fuzzy sets. FLM theory not only enriches and improves the PLS theory, and also provides new research methods for the theory research such as pattern recognition, machine learning, clustering analysis and so on.
Year
DOI
Venue
2008
10.1109/CSSE.2008.1318
CSSE (1)
Keywords
Field
DocType
fuzzy set theory,fuzzy set,fuzzy likelihood function,pattern recognition,current psm theory,pattern similarity measure,flm theory,fuzzy subsets degree,clustering analysis,fuzzy likelihood measure algorithm,theory research,fuzzy sets,similarity measure,machine learning,pls theory,axiomatic theory,fuzzy likelihood measure,novel fuzzy likelihood measure,psm theory,algorithm design and analysis,barium,cluster analysis,likelihood function,integrated circuits
Data mining,Fuzzy clustering,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy set,Artificial intelligence,Fuzzy number,Pattern recognition,Defuzzification,Fuzzy measure theory,Algorithm,Membership function,Machine learning
Conference
Volume
ISBN
Citations 
1
978-0-7695-3336-0
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Shifei Ding1107494.63
Fengxiang Jin212410.72