基本事实
计算机科学
地标
人工智能
集合(抽象数据类型)
回归
任务(项目管理)
职位(财务)
对比度(视觉)
模式识别(心理学)
线性回归
机器学习
统计
数学
经济
管理
程序设计语言
财务
作者
Benjamin Johnston,Philip de Chazal
标识
DOI:10.1109/embc46164.2021.9629710
摘要
We propose a novel method for deriving ground truth labels for regression problems that considers the precision of annotators separately for each label. This method ensures that higher performing annotators contribute more to the final landmark position which is in contrast to conventional methods that assume all annotators are equally accurate in completing the set task. In addition to describing the novel method, a set of preliminary experimental results is also provided, comparing the performance of the precision method to that of the global mean.
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