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ADAPTIVE WEIGHTING OF SIMILARITY METRICS FOR PREDICTIVE ANALYTICS OF A COGNITIVE SYSTEM

机译:相似系统的预测分析相似度的自适应加权

摘要

According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.
机译:根据本发明的实施例,可以使用自适应加权方案来组合相似性度量或相似性度量。从具有已知关系的第一组实体中随机选择一个子集。子集与彼此和/或与第一组实体具有未知关系的第二组实体组合。对于第二集合中的每个实体,确定第一集合与组合的第二集合(包括子集)之间的至少两个不同的相似性度量(相似性度量)。对于第二组中的每个实体,比较至少两个不同的相似性度量,并且基于相似性度量的大小,将权重自适应地分配给每个相似性度量。组合加权的相似性度量,以确定每个实体的合计自适应加权相似性得分。

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