Instance-Based Learning of Credible Label Sets

机译：基于实例的可信标签集学习

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Even though instance-based learning performs well in practice, it might be criticized for its neglect of uncertainty: An estimation is usually given in the form of a predicted label, but without characterizing the confidence of this prediction. In this paper, we propose an instance-based learning method that allows for deriving "credible" estimations, namely set-valued predictions that cover the true label of a query object with high probability. Our method is built upon a formal model of the heuristic inference principle underlying instance-based learning.

机译：即使基于实例的学习在实践中表现良好，也可能因忽略不确定性而受到批评：估计通常以预测标签的形式给出，但没有体现这种预测的可信度。在本文中，我们提出了一种基于实例的学习方法，该方法允许派生“可信”估计，即具有较高概率覆盖查询对象真实标签的集值预测。我们的方法基于基于实例的学习的启发式推理原理的形式模型。

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《26th Annual German Conference on AI(Artificial Intelligence) KI 2003; Sep 15-18, 2003; Hamburg, Germany》|2003年|p.450-464|共15页
会议地点 Hamburg(DE);Hamburg(DE);Hamburg(DE);Hamburg(DE)
作者
Eyke Huellermeier;
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作者单位

Informatics Institute, Marburg University, Germany;

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原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
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Instance-Based Learning of Credible Label Sets

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