A Dataset Complexity Measure for Analogical Transfer
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.
Cite
Text
Badra. "A Dataset Complexity Measure for Analogical Transfer." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/222Markdown
[Badra. "A Dataset Complexity Measure for Analogical Transfer." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/badra2020ijcai-dataset/) doi:10.24963/IJCAI.2020/222BibTeX
@inproceedings{badra2020ijcai-dataset,
title = {{A Dataset Complexity Measure for Analogical Transfer}},
author = {Badra, Fadi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {1601-1607},
doi = {10.24963/IJCAI.2020/222},
url = {https://mlanthology.org/ijcai/2020/badra2020ijcai-dataset/}
}