A Novel Fuzzy Likelihood Measure Algorithm

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.