A biparametric family of cardinality-based fuzzy similarity measures

We present a systematic way of constructing and analyzing fuzzy similarity measures based on cardinality. This is achieved by introducing a general form for such measures, that depends on two parameters. We demonstrate that this general form includes several existing families of fuzzy similarity measures. Moreover, we show that certain properties can be ensured by imposing simple constraints on the parameters. In particular, we present constraints that ensure several forms of restrictability, which allow to reduce the calculation time in practical implementations. To conclude, we illustrate the presented technique by using it to analyze some well-known fuzzy similarity measures

A triparametric family of cardinality-based fuzzy similarity measures

Previously, we introduced a biparametric family of cardinality-based fuzzy similarity measures. In this paper, we generalize this family by adding a third parameter. We also study the generalized family for specific values of the third parameter. More precisely, we show that for particular values of this parameter, certain properties can be ensured by imposing constraints on the two remaining parameters. To conclude, we examine some members of the presented family of fuzzy similarity measures

COMBINING AUDIO SIMILARITY MEASURES USING GENERALIZATIONS OF LOGICAL CONNECTIVES

A triangular operator is an aggregation operator that can be understood as a generalization of a logical connective. Although weighted means are very often used for combining audio similarity measures, theoretical considerations suggest that it might be better to use a triangular operator instead. With the MIREX 2007 submissions described in this paper, we investigate if triangular operators really are suitable for combining audio similarity measures.

A BIPARAMETRIC FAMILY OF CARDINALITY-BASED FUZZY SIMILARITY MEASURES

We present a systematic way of constructing and analyzing fuzzy similarity measures based on cardinality. This is achieved by introducing a general form for such measures, that depends on two parameters. We demonstrate that this general form includes several existing families of fuzzy similarity measures. Moreover, we show that certain properties can be ensured by imposing simple constraints on the parameters. In particular, we present constraints that ensure several forms of restrictability, which allow to reduce the calculation time in practical implementations. To conclude, we illustrate the presented technique by using it to analyze some well-known fuzzy similarity measures.Fuzzy similarity measures, fuzzy cardinality