Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data-Reference-Cited by-同舟云学术

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data

Published:2021 Issue: Volume: Page:152-163
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
language:
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Author:

Hanselle Jonas^ORCID,Tornede Alexander^ORCID,Wever Marcel^ORCID,Hüllermeier Eyke^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-75762-5_13

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5. Gagliolo, M., Legrand, C.: Algorithm survival analysis. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds.) Experimental Methods for the Analysis of Optimization Algorithms, pp. 161–184. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-02538-9_7

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