THEORY AND ALGORITHM OF FUZZY LIKELIHOOD MEASURE

摘要：

Pattern Similarity Measure(PSM) is a kind of measurement that measures the size of similarity between two patterns, it plays a key role in the analysis and research of fuzzy 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 measures, give the frequentlyused 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 PSM theory, and also provides new research methods for the theory research such as fuzzy pattern recognition, machine learning, clustering analysis and so on.

关键词： Pattern Similarity Measure(PSM) fuzzy likelihood measure (FLM) fuzzy sets fuzzy pattern recognition (FPR).

作者: Shifei Ding Fengxiang Jin Xinzheng Xu Li Wang Qing He

作者单位: School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221008 Ch College of Geoinformation Science and Engineering,Shandong University of Science and Technology,Qing School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221008 Ch Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Acade

会议类型: 国际会议

会议名称: 2008高等智能国际会议(2008 International Conference on Advanced Intelligence)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-18（万方平台首次上网日期，不代表论文的发表时间）