Aiming at the weight computing problem in the group analytic hierarchy process (AHP), logarithmic regress is adopted.
针对群体层次分析法的权重计算问题，采用对数回归法求解。
In this model, there are two factors affecting the description's precision: one is the choice of the feature words; another is the method of weight computing.
在这个模型中，有两个主要影响描述准确度的因素：一个是特征项的选择，一个是特征项的权重计算方式。
This includes the algorithms for computing a weight array, the way to reconstruct distorted patterns, and the algorithm for computing a pattern's energy level.
这包括计算权重数组的算法、重构失真图案的方式以及计算图案的能量级别的算法。
The method of unfolding concepts of ontology to get a set of keywords with semantics and then computing the weight-value of the keywords to acquire the ontology index is proposed in this paper.
针对当前本体搜索中存在的问题，提出了一种通过拆分概念来获取语义关键词进而通过计算权值来获得一组本体特征指数的方法。
Both of attribute weight frequency and strong compressible set are used to simplify discernibility matrix so that computing complexity is decreased and reduction efficiency is.
同时利用属性加权频率和强等价集概念化简区分矩阵，既减小了计算复杂度又提高了约简效率。
Disjoint paths weight accumulating algorithm is proved to be effective in correlation computing by analyzing the results of the experiments.
分析实验结果发现，不相交路径权值累积算法能有效计算网络成员关联度。
Both of attribute weight frequency and strong compressible set are used to simplify discernibility matrix so that computing complexity is decreased and reduction efficiency is increased.
同时利用属性加权频率和强等价集概念化简区分矩阵，既减小了计算复杂度又提高了约简效率。
Through adjusting weight, computing error rate and modifying the parameters of hidden nodes, optimal results will be achieved in the learning procedure.
学习过程通过调整权值、计算误差、修正隐层单元的参数，以达到最优结果。
In the process of understanding an image, people pay more attention to focus region. Focus region of an image can be extracted by computing visual attention weight of image regions.
由于人们对图像的认识过程中，对焦点区域有比较多的关注，因此可以通过视觉焦点权重模型计算图像各区域的视觉焦点权重来提取图像的焦点区域。
In the process of understanding an image, people pay more attention to focus region. Focus region of an image can be extracted by computing visual attention weight of image regions.
由于人们对图像的认识过程中，对焦点区域有比较多的关注，因此可以通过视觉焦点权重模型计算图像各区域的视觉焦点权重来提取图像的焦点区域。
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