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  relative entropy
    With the concept of entropy as the visual feature representation , we propose two methods of similarity measurement: entropy difference distance measure(ED) and maximum relative entropy distance measure(MRE) .
    基于信息熵作为可视化特征表示这一概念,本文提出了两种相似性度量方法:熵差分距离度量和最大相关熵度量。
短句来源
  relative entropy
    With the concept of entropy as the visual feature representation , we propose two methods of similarity measurement: entropy difference distance measure(ED) and maximum relative entropy distance measure(MRE) .
    基于信息熵作为可视化特征表示这一概念,本文提出了两种相似性度量方法:熵差分距离度量和最大相关熵度量。
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  relative entropy
Let (Xn)n∈N be a sequence of arbitrary continuous random variables, by the notion of relative entropy
      
On Density Estimation under Relative Entropy Loss Criterion
      
A density estimation problem under relative entropy loss criterion is considered.
      
We give a proof of the additivity conjecture for a quantum depolarizing channel Φ based on the decreasing property of the relative entropy.
      
Relative entropy as a measure of the degree of mixing in noisy systems
      
更多          
  relative entropy
Let (Xn)n∈N be a sequence of arbitrary continuous random variables, by the notion of relative entropy
      
On Density Estimation under Relative Entropy Loss Criterion
      
A density estimation problem under relative entropy loss criterion is considered.
      
We give a proof of the additivity conjecture for a quantum depolarizing channel Φ based on the decreasing property of the relative entropy.
      
Relative entropy as a measure of the degree of mixing in noisy systems
      
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  其他


To classify wood by surface texture,wood texture parameters of Gray Level Co-occurrence Matrix(GLCM) were first selected by relevance analysis.The building rules of GLCM(the best gray levels and pixels interval) were confirmed by experiment.GLCM texture parameters of 300 wood specimens were calculated and assayed,and then placed into a Competitive Neural Network for classification output.The results are as follows: 1) GLCM parameters "Angular Second Moment"," contrast "," correlation"," entropy","sum of squares...

To classify wood by surface texture,wood texture parameters of Gray Level Co-occurrence Matrix(GLCM) were first selected by relevance analysis.The building rules of GLCM(the best gray levels and pixels interval) were confirmed by experiment.GLCM texture parameters of 300 wood specimens were calculated and assayed,and then placed into a Competitive Neural Network for classification output.The results are as follows: 1) GLCM parameters "Angular Second Moment"," contrast "," correlation"," entropy","sum of squares " and " inverse difference moment" are suitable to describe the wood texture,2) the best pixels interval when building GLCM is 4 when the wood image size is 512×512 and proportion is 1∶1,and 128 levels image gray can best reflect the wood texture information,3) the texture direction is exactly the direction of the maximum value of 'Angular Second Moment','correlation' and 'entropy' of the GLCM of the wood texture image,and 4) the correct rate of classification of the Competitive Neural Network classifier is 88 percent.The six parameters of GLCM building rules mentioned above are valid to describe wood texture feature and it is workable to classify wood by surface texture according the six GLCM parameters.

为了对木材进行表面纹理分类,首先确定纹理的灰度共生矩阵描述参数、灰度共生矩阵的生成像素间距和灰度级数;求取分析了200个木材样本的纹理参数并输入给竞争神经网络进行分类验证.实验表明:1)以“角二阶矩”、“对比度”、“相关”、“熵”、“方差”、“逆差矩”作为描述木材纹理的特征参数是合适的.2)在比例为1∶1的512×512木材图像情况下,生成灰度共生矩阵的最佳像素间距为4,最佳图像灰度级数为128.3)木材纹理图像灰度共生矩阵的"角二阶矩"、"相关"和"熵"值最大的方向为纹理方向.4)竞争神经网络的分类正确率为88%.研究结论:按上述规则生成的6个灰度共生矩阵参数对描述木材表面纹理特征是有效的,据此对木材表面纹理分类是可行的.

>=Semantic similarity is one of the basic relations between words, the research on the measure of semantic similarity between Chinese words takes an important role in Chinese language processing applications such as information retrieval, statistical language modeling, etc. In this paper, the mathematical model of semantic similarity between words is presented, and a statistical measurement of semantic similarity between Chinese words is described, elementary' experiments show that this method is a theoretical...

>=Semantic similarity is one of the basic relations between words, the research on the measure of semantic similarity between Chinese words takes an important role in Chinese language processing applications such as information retrieval, statistical language modeling, etc. In this paper, the mathematical model of semantic similarity between words is presented, and a statistical measurement of semantic similarity between Chinese words is described, elementary' experiments show that this method is a theoretical approach and effective in practice.

语义相似是词汇间的基本关系之一,汉语词汇间语义相似的定量化研究对于信息、检索、统计语言模型等自然语言处理的应用技术只有重要的指导意义。本文定义了语义相似度的数学模型,进而描述了基于相关熵的汉语词汇间语义相似度计算方法。初步实验表明,该方法是一种理论基础严整,实践上行之有效的方法。

 
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