Learning by Ignoring the Most Wrong

S. Bradley
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引用次数: 4

Abstract

Abstract Imprecise probabilities (IP) are an increasingly popular way of reasoning about rational credence. However they are subject to an apparent failure to display convincing inductive learning. This paper demonstrates that a small modification to the update rule for IP allows us to overcome this problem, albeit at the cost of satisfying only a weaker concept of coherence.
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通过忽略最大的错误来学习
不精确概率(IP)是一种越来越流行的关于理性可信度的推理方法。然而,他们显然无法表现出令人信服的归纳学习。本文证明了对IP更新规则的一个小修改可以让我们克服这个问题,尽管代价是只满足较弱的一致性概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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