利用度量矩阵距离几何法计算了其三维空间结构，并进行了结构的优化。
Twelve structures with lower energy was obtained via metric matrix distance geometry and refinement with simulated annealing.
为此，提出了一种新的匹配判决算法——基于匹配度量矩阵扰动校正的多对一匹配判决算法。
To solve this problem, a new algorithm namely many-to-one matching decision algorithm based on correction of perturbation for matching measure matrix is proposed.
通过分析判断矩阵，一致性矩阵，导出矩阵及度量矩阵的关系，提出一种修改判断矩阵的预测加速修正的贪婪算法。
Through analyzing relation judgment matrix, consistency matrix, induced matrix and measure matrix, a prediction accelerating greedy algorithms to rectified element is put forword.
其次，论文研究了通过“断点分辨矩阵”来度量候选断点重要性的启发式方式。
Subsequently, to heuristically measure the relative importance of candidate cuts, relevant metrics are studied based on "cut discernibility matrix".
度量候选断点的重要性不但要考虑该矩阵的列方向特征，而且还要以适当的方式考虑矩阵的行方向特征。
When measuring the importance of a candidate cut, both the characteristics of columns and rows of this matrix should be reasonably taken into consideration.
进而提出了基于混淆矩阵度量模式间混淆关系的方法。
Then a measurement of this relationship has been proposed by utilizing the confusion matrix.
在这种相似性度量方法基础上，证明了粗集的等价关系可以被转化为模糊等价矩阵。
Based on the method of similarity measure, it proves that the equivalence relation of rough set can be transformed into fuzzy equivalence matrix comp.
该方法利用相似系数在各构件的耐久性指标序列之间进行相似性度量，并形成了具有分类功能的模糊等价矩阵。
The method measures the similarity of durability index series of components by the use of similarity coefficient and forms a fuzzy equivalence matrix with sort function.
根据支持度矩阵，获得各传感器在不同时刻的一致性度量。
Based on the support degree matrix, each sensor's consistency value of different time can be obtained.
该算法是用高斯相似度度量协方差矩阵间的距离，并由此测度建立了反映协方差矩阵结构关系的二叉决策树。
Gaussian similarity is used for measuring the distance of different covariance. A binary decision tree is constructed with this measure.
最后采用基于矩阵的F -范数代替传统的基于向量的2一范数进行分类度量。
Last but more importantly, we used the F-norm classification measure based on the matrix instead of the traditional 2-norm measure based on the vector.
然后把等价类映射为矩阵表达形式，再通过矩阵的特性来度量影响片段相似度的不同因子，实现了相似片段的排序。
Afterwards, equivalence classes are mapped into matrix representation. Therefore various factors are computed to rank the similarity of the selected video clips by characters of the matrix.
然后，对所建立关联矩阵的列向量或行向量进行相似性度量，获得相似客户群体或相关页面。
Then, similar customer groups or relevant Web pages were obtained by measuring the similarity between column vectors or between row vectors of the associated matrix.
然后，对所建立关联矩阵的列向量或行向量进行相似性度量，获得相似客户群体或相关页面。
Then, similar customer groups or relevant Web pages were obtained by measuring the similarity between column vectors or between row vectors of the associated matrix.
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