Research Publications for Bernhard M Pfahringer
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Barracchia, E. P., Pio, G., Bifet, A., Gomes, H. M., Pfahringer, B., & Ceci, M. (2022). LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding. Information Sciences, 606, 702-721. doi:10.1016/j.ins.2022.05.079
Wang, H., Gouk, H., Fraser, H., Frank, E., Pfahringer, B., Mayo, M., & Holmes, G. (2022). Experiments in cross-domain few-shot learning for image classification. Journal of the Royal Society of New Zealand, 1-23. doi:10.1080/03036758.2022.2059767
Other publications by:
FRANK, Eibe T :: PFAHRINGER, Bernhard M :: MAYO, Michael J :: HOLMES, GeoffreyWang, H., Frank, E., Pfahringer, B., Mayo, M., & Holmes, G. (2022). Cross-domain Few-shot Meta-learning Using Stacking. arXiv. Retrieved from http://arxiv.org/abs/2205.05831v1
Sahito, A., Frank, E., & Pfahringer, B. (2022). Better self-training for image classification through self-supervision. In G. Long, X. Yu, & S. Wang (Eds.), Proc 34th Australisian Joint Conference on Advances in Artificial Intelligence (AI 2021), LNAI 13151 (pp. 645-657). Sydney, Australia: Springer. doi:10.1007/978-3-030-97546-3_52
Yogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2022). Predicting COVID-19 patient shielding: A comprehensive study. In Proc 34th Australasian Joint Conference on Advances in Artificial Intelligence (AI 2021) LNCS 13151 (pp. 332-343). Sydney, Australia: Springer International Publishing. doi:10.1007/978-3-030-97546-3_27
Yogarajan, V., Pfahringer, B., Smith, T., & Montiel, J. (2021). Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents. arXiv:. Retrieved from http://arxiv.org/abs/2112.01718v1
Mastelini, S. M., Montiel, J., Gomes, H. M., Bifet, A., Pfahringer, B., & De Carvalho, A. C. P. L. F. (2021). Fast and lightweight binary and multi-branch Hoeffding Tree Regressors. In IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December (pp. 380-388). doi:10.1109/ICDMW53433.2021.00053
Chanajitt, R., Pfahringer, B., & Gomes, H. M. (2021). Combining static and dynamic analysis to improve machine learning-based malware classification. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics. doi:10.1109/DSAA53316.2021.9564144
Gouk, H., Frank, E., Pfahringer, B., & Cree, M. J. (2021). Regularisation of neural networks by enforcing Lipschitz continuity. Machine Learning, 110, 393-416. doi:10.1007/s10994-020-05929-w
Open Access version at:
https://hdl.handle.net/10289/14147
Other publications by:
FRANK, Eibe T :: PFAHRINGER, Bernhard M :: CREE, Michael JJia, Y., Frank, E., Pfahringer, B., Bifet, A., & Lim, N. (2021). Studying and exploiting the relationship between model accuracy and explanation quality. In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science Vol. 12976 (pp. 699-714). Cham: Springer. doi:10.1007/978-3-030-86520-7_43
Open Access version at:
https://hdl.handle.net/10289/14561
Other publications by:
BIFET FIGUEROL, Albert C :: JIA, Yunzhe (Alvin) :: FRANK, Eibe T :: PFAHRINGER, Bernhard M :: LIM, Jin Sean S (Nick)
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