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By nature,it is not debt in itself,but possible debt with concealment and uncertainty.

从本质上讲,它本身并不是"债务",而只是可能的债务,具有隐匿性和不确定性

To settle conclusively all contention or uncertainty about.

就…的争辩或不确定性做出最后的决定。

Based on the conditional rough entropy theory, the definition of the conditional rough entropy of set is given and it can measure the uncertainty of set. The properties and the related theorems are given.

基于知识的条件粗糙熵理论,提出了集合的条件粗糙熵概念,用来刻画和度量集合的不确定性

Examples are given to illustrate the conditional entropy maybe increased,so Shannon's definition of information is not absolutely correct.

从新的角度指出了香农信息论的局限性,指出了它没有考虑信息的可靠性,完备性等特点,指出条件熵的计算公式问题并且进行了纠正,并且举例证明了条件熵可能增加,进而香农信息定义中的不确定性的减少也不是绝对的。

The theory significance of conditional rough entropy in decision information system is discussed. The conditional rough entropy measures the partial uncertainty in decision information systems.

讨论了集合的条件熵在目标信息系统中的理论意义,集合的条件熵刻画了目标信息系统的局部的不确定性

Examples are given to illustrate the conditional entropy maybe increased, so Shannon's definition of information is not absolutely correct.

论文摘要:从新的角度指出了香农信息论和信息定义的局限性,指出了它没有考虑信息的可靠性,完备性等特点,指出条件熵的命名不恰当,香农的"条件熵"应该称为加权平均条件熵,并且举例证明了条件熵可能增加,进而香农信息定义中的不确定性的减少也不是绝对的。

Examples are given to prove the conclusion that conditional entropy never increases is not absolute, thus the representation that information is to decrease uncertainty in the definition of information is not absolutely correct, so Shannon's definition of information is not fit.

从多个角度分析了条件熵的定义和计算公式的问题,对条件熵的计算公式进行了纠正,并且举例证明了熵不增并不是绝对的,进而指出香农信息定义中的不确定性的减少也不是绝对的,从而信息的定义名不副实。

The basic idea is that we use entropy and conditional entropy to measure whether a feature type grasps some of the information for syntactic structure prediction.

如果加入某个特征类型之后当前句法结构的不确定性明显下降,则认为该特征类型抓住了上下文中影响句法结构的某些主要信息。

This paper analyzes the informational entropy and the conditional entropy,and studies the entropy deeply as an indefinite measure of the battlefield information.

熵是度量信息有效性和可靠性的重要理论工具,分析了信息熵和条件熵,并深入研究了熵作为不确定性的度量在战场情报中的评估应用,最后举例说明了既要考虑信息的价值又要考虑其风险程度这一新的情报分析方法,使得决策过程透明化,降低了决策的风险。

Limitations of Shannon's information theory and information definition are analyzed from new angles. It is pointed out that they didn't consider the reliability, completeness and other characteristics of information, and the naming of conditional entropy is not proper. The correct naming of Shannon's "conditional entropy" should be weighted average conditional entropy. Examples are given to illustrate the conditional entropy maybe increased, so Shannon's definition of information is not absolutely correct.

从新的角度指出了香农信息论和信息定义的局限性,指出了它没有考虑信息的可靠性,完备性等特点,指出条件熵的命名不恰当,香农的"条件熵"应该称为加权平均条件熵,并且举例证明了条件熵可能增加,进而香农信息定义中的不确定性的减少也不是绝对的。

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