The fuzzy favorite of decision participant is expressed by means of subject function, and the decision-making is studied according to the space distance of fuzzy favorite relations.
利用隶属函数来表达决策参与者的模糊偏好，将个人偏好集成为群偏好，根据模糊偏好关系空间距离来研究决策制定。
It maps original data to kernel space to get a kernel matrix, and utilizes kernel function and L1 norm to minimize the distance function.
运用核函数将原始数据映射到核空间中得到核矩阵，再利用L1范数使距离函数达到最小。
This paper gives the metrizable conditions of 2-probabilistic metric space (2-pm space), its distance function and pseudo distance function, thus extends some conclusions of PM-space to 2-pm space.
本文给出了2—PM空间的可度量化条件及度量函数、伪度量函数，从而把PM空间上的有关结论推广到2—PM空间上。
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