公制(单位)
登普斯特-沙弗理论
度量(数据仓库)
口译(哲学)
相似性(几何)
计算机科学
相似性度量
鉴定(生物学)
算法
人工智能
数学
数据挖掘
图像(数学)
运营管理
植物
生物
经济
程序设计语言
作者
Anne-Laure Jousselme,Dominic Grenier,Éloi Bossé
标识
DOI:10.1016/s1566-2535(01)00026-4
摘要
Abstract We present a measure of performance (MOP) for identification algorithms based on the evidential theory of Dempster–Shafer. As an MOP, we introduce a principled distance between two basic probability assignments (BPAs) (or two bodies of evidence) based on a quantification of the similarity between sets. We give a geometrical interpretation of BPA and show that the proposed distance satisfies all the requirements for a metric. We also show the link with the quantification of Dempster's weight of conflict proposed by George and Pal. We compare this MOP to that described by Fixsen and Mahler and illustrate the behaviors of the two MOPs with numerical examples.
科研通智能强力驱动
Strongly Powered by AbleSci AI