Similarity Assessment for Generalizied Cases by Optimization Methods
Similarity Assessment for Generalizied Cases by Optimization Methods
复制标题
通过优化方法进行广义案例的相似性评估
DOI：
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发表时间：
2002
期刊：
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通讯作者：
R. Bergmann
中科院分区：
文献类型：
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作者：
Babak Mougouie;R. Bergmann
广义情形是覆盖问题解空间中的子空间而不是点的情形。广义事例可以用事例属性上的一组约束来表示。对于这样的表示，点查询和广义情况之间的相似性评估是一个困难的问题，在本文中解决。该任务是找到点查询和广义情况下所覆盖的区域的最近点之间的距离（或相关的相似性），相对于一些给定的相似性度量。我们制定这个问题作为一个数学优化问题，我们提出了一个新的切割平面的方法，使我们能够排名广义的情况下，根据他们的距离查询。
Generalized cases are cases that cover a subspace rather than a point in the problem-solution space. Generalized cases can be represented by a set of constraints over the case attributes. For such representations, the similarity assessment between a point query and generalized cases is a difficult problem that is addressed in this paper. The task is to find the distance (or the related similarity) between the point query and the closest point of the area covered by the generalized cases, with respect to some given similarity measure. We formulate this problem as a mathematical optimization problem and we propose a new cutting plane method which enables us to rank generalized cases according to their distance to the query.