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- W3034856945 abstract "Analogical transfer consists in leveraging a measure of similarity between two situations to predict the amount of similarity between their outcomes. Acquiring a suitable similarity measure for analogical transfer may be difficult, especially when the data is sparse or when the domain knowledge is incomplete. To alleviate this problem, this paper presents a dataset complexity measure that can be used either to select an optimal similarity measure, or if the similarity measure is given, to perform analogical transfer: among the potential outcomes of a new situation, the most plausible is the one which minimizes the dataset complexity." @default.
- W3034856945 created "2020-06-19" @default.
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- W3034856945 date "2020-07-01" @default.
- W3034856945 modified "2023-10-01" @default.
- W3034856945 title "A Dataset Complexity Measure for Analogical Transfer" @default.
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- W3034856945 doi "https://doi.org/10.24963/ijcai.2020/222" @default.
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