摘要翻译：
由经典信息公式推导出与逻辑概率和模糊集有关的广义信息公式。新的信息测度很符合波普尔的知识进化准则。与平方误差判据相比，信息判据不仅反映命题的误差，而且反映命题所描述事件的特殊性。它给出了一个逻辑概率较小的命题较高的评价。本文介绍了如何根据广义信息准则从众多的预测和语言翻译中选择一个预测或句子进行预测和语言翻译。为了提高数据压缩和通信效率，引入了速率保真度理论，它是对经典信息论中率失真理论的改进，用广义互信息准则代替失真（即平均误差）准则。从速率保真度函数中得到了与图像通信有关的一些有趣的结论。并讨论了如何完善波普尔的理论。
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英文标题：
《A Generalized Information Formula as the Bridge between Shannon and
  Popper》
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作者：
Chenguang Lu
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  A generalized information formula related to logical probability and fuzzy set is deduced from the classical information formula. The new information measure accords with to Popper's criterion for knowledge evolution very much. In comparison with square error criterion, the information criterion does not only reflect error of a proposition, but also reflects the particularity of the event described by the proposition. It gives a proposition with less logical probability higher evaluation. The paper introduces how to select a prediction or sentence from many for forecasts and language translations according to the generalized information criterion. It also introduces the rate fidelity theory, which comes from the improvement of the rate distortion theory in the classical information theory by replacing distortion (i.e. average error) criterion with the generalized mutual information criterion, for data compression and communication efficiency. Some interesting conclusions are obtained from the rate-fidelity function in relation to image communication. It also discusses how to improve Popper's theory.
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PDF链接：
https://arxiv.org/pdf/0707.3457