提出新的条件信息熵及其高效知识约简算法。
A new conditional entropy and knowledge reduction algorithms are proposed.
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。
A heuristic algorithm based on conditional information entropy for knowledge reduction is proposed, and the complexity of this algorithm is analyzed.
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。
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.
该算法直接在基因的层面上进行优化，能学习劣解的基因，并用信息熵作为结束条件的判据。
GOA optimizes directly at the gene level and can learn from the gene of bad individuals. The entropy is used for the terminal criterion of the algorithm.
简要地介绍了不确定性、信息熵、联合熵、条件熵、互信息的基本概念。
The basic concepts of uncertainty, information entropy, united entropy, term entropy and mutual information were introduced briefly.
依据粗集理论研究离散化数据的特点，考虑类分布信息，采用信息熵理论进行连续条件属性的离散化。
Data discrimination is the character of RS, considering distributed information of class, and continual condition attributes are described according to information entropy theory.
文字符号的极限熵是在充分考虑上下文信息条件下，字符所包含平均信息量的大小。
Ultimate entropy is the average information per character, taking the sufficient context into consideration.
文字符号的极限熵是在充分考虑上下文信息条件下，字符所包含平均信息量的大小。
Ultimate entropy is the average information per character, taking the sufficient context into consideration.
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