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conditional clause相关的网络例句

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与 conditional clause 相关的网络例句 [注:此内容来源于网络,仅供参考]

The roughness of knowledge embodies the granularity of knowledge in algebra view.Two concepts are defined in information view,which are information entropy of knowledge and conditional information entropy of knowledge.

在代数观点中,知识粗糙性体现了知识的粒度;而在信息观中,定义了知识的信息熵和条件信息熵。

Conditional information entropy was used to compute relevance of attributes and it was used in fitness function to assure reduction has fewer attributes and relevance of attributes.

利用条件墒计算属性问的相关性,并将其引入到适值函数中,可以保证所求约简含有较少的属性而且属性间的相关性较小。

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。

Its feasibility was proved by calculation conditional entropy of 2step and korder Markov predictor.

通过分别对二步和k阶Markov预测器条件熵的计算,在理论上了证明了二步Markov预测器的可行性。

At first, analyze the farmer's desire for the pension by Rough Set theory, construct the attribute reduction algorithm based on the conditional entropy for the computation and analysis of 8,410 questionnaire surveys data to make use of the advantage of rough set to deal with indefinite and incomplete data.

首先利用粗糙集理论理不确定和不完整数据的优势,基于改进的基于条件熵的属性约简理论对8410份问卷调查数据进行计算与分析,得出与农民参保意愿密切相关的几个因素,如年龄、性别、收入、行业等。

To solve that problem, the collocation preference statistic model based on relative conditional entropy is brought up in this paper to measure dependence between headword and co-occurrence words in context.

针对以往研究将搭配视为词项的简单并置,而没有考虑词项间的倾向性的问题,提出了一个基于相对条件熵的搭配倾向统计模型,衡量中心词对上下文同现词的依赖程度。

Using conditional entropy as the heuristic information, it does not compute the core attribute, attribute reduction starts from the condition attributes directly.

该算法利用条件熵度量属性的重要性,不计算条件属性的核,直接在条件属性的基础上约简。

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。

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文章在说明了微电路模块自动测试系统的总体设计后,对GPIB标准进行了介绍。

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