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5 Publications
2023 | Journal Article | LibreCat-ID: 48777
F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “Incremental permutation feature importance (iPFI): towards online explanations on data streams,” Machine Learning, 2023, doi: 10.1007/s10994-023-06385-y.
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 50262
F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “Incremental permutation feature importance (iPFI): towards online explanations on data streams,” Machine Learning, vol. 112, no. 12, pp. 4863–4903, 2023, doi: 10.1007/s10994-023-06385-y.
LibreCat
| DOI
2021 | Journal Article | LibreCat-ID: 25035
B. Haddenhorst, V. Bengs, and E. Hüllermeier, “On testing transitivity in online preference learning,” Machine Learning, pp. 2063–2084, 2021, doi: 10.1007/s10994-021-06026-2.
LibreCat
| DOI
2021 | Journal Article | LibreCat-ID: 66216
J. Shi et al., “MODES: model-based optimization on distributed embedded systems,” Machine Learning, vol. 110, no. 6, pp. 1527–1547, 2021, doi: 10.1007/s10994-021-06014-6.
LibreCat
| DOI
2018 | Journal Article | LibreCat-ID: 3402
V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.
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