A heuristic algorithm based on conditional information entropy for knowledge reduction is proposed, and the complexity of this algorithm is analyzed.
提出了一种基于条件信息熵的知识约简启发式算法，并指出该算法的时间复杂度是多项式的。
The concept of rough entropy is proposed. The monotony between the uncertainty of knowledge in the rough set theory and its corresponding rough entropy is proved.
提出了粗糙信息熵的概念，证明了粗糙集理论中知识不确定性与其所对应的粗糙信息熵之间的单调关系。
Information storage, entropy and connotation reflect three guidelines of an enterprise, that is, inner knowledge capacity, systematic sequence and content.
知识存量、信息熵、知识语义是反映企业内部知识容量，系统有序性以及内容的三个指标。
The formula of the prior knowledge classification standard was deduced used of the information entropy and the condition entropy formula, which add the priori knowledge parameter to the weighting sum.
论文中利用信息熵、条件熵公式推导出先验知识度划分标准公式，该公式把信息熵公式中加权和转换为加权和加先验知识度参数。
Then the membership matrix obtained by clustering algorithm was used to reduce attribute set. Finally, based on entropy, a knowledge acquisition method of fuzzy Rough Set (RS) was put forward.
进而将聚类得到的属性隶属矩阵用于属性约简，并提出一种基于信息熵的模糊粗糙集知识获取的方法。
A new conditional entropy and knowledge reduction algorithms are proposed.
提出新的条件信息熵及其高效知识约简算法。
And the human, the main part of the intelligence-circle, can bring minus entropy in it by our wisdom and knowledge to slow the entropy growing speed down.
而人类作为智慧圈的主体，是可以通过智慧和知识，向其输入负熵，以延缓熵增速度。
The open system of organization can draw negative entropy from exterior environment on one hand, but more important, the organization in the era of knowledge economy can accumulate and co...
组织系统的开放性一方面可以使组织系统从外部环境中吸收负熵，但更为重要的来自另一方面，知识经济时代的组织可以通过组织的学习来积聚和复合知识和信息。
Maximum bounded entropy principle is employed concerning the prior knowledge of binary image, the maximum bounded entropy restoration method with binary constraint is put forword.
运用最大有界熵概念，提出了二值约束的最大有界熵复原法。
Based on the study and research of the basic knowledge, this article focuses on the information entropy-based worm detection algorithm.
在前述基础知识的学习研究基础之上，本文重点对基于信息熵的蠕虫检测算法进行了一系列研究。
We propose a new approach to multivariate decision tree construction based on knowledge roughness in rough set instead of information entropy as usual.
提出了一种基于粗糙集中知识粗糙度的构建多变量决策树的算法。
We propose a new approach to multivariate decision tree construction based on knowledge roughness in rough set instead of information entropy as usual.
提出了一种基于粗糙集中知识粗糙度的构建多变量决策树的算法。
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