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CONSTRUING SIMILARITIES BETWEEN DATASETS WITH EXPLAINABLE COGNITIVE METHODS

机译:具有可解释的认知方法的数据集之间的约束相似之处

摘要

In an approach for construing similarities between datasets, a processor accesses a pair of sets of feature weights, wherein the sets of feature weights include a query dataset and comprises first weights associated to first features and a reference dataset and comprises second weights associated to second features. Based on similarities between the first features and the second features, a processor discovers flows from the first features to the second features, wherein the flows maximize an overall similarity between the pair of sets of feature weights. Based on the similarities and the flows, a processor computes pair contributions to the overall similarity in order to obtain contributive elements, wherein the pair contributions are contributions of pairs joining the first features to the second features. A processor ranks the contributive elements to obtain respective ranks. A processor returns a result comprising the contributive elements and indications to the respective ranks.
机译:在用于限制数据集之间的相似性的方法中,处理器访问一对特征权重,其中,该组特征权重集包括查询数据集,并且包括与第一特征和参考数据集相关联的第一权重,并且包括与第二特征相关联的第二权重 。 基于第一特征和第二特征之间的相似性,处理器发现从第一特征到第二特征的流程,其中流程最大化了一对特征权重之间的总体相似性。 基于相似之处和流量,处理器计算对整体相似性的对贡献,以便获得贡献元件,其中,该对贡献是将第一特征的对的贡献与第二特征相结合。 处理器排名有助的元素以获得各自的级别。 处理器返回一个结果,该结果包括对各个等级的贡献元素和指示。

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