Research Publications for Bernhard M Pfahringer
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Wang, 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
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
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 Lecture Notes in Computer Science (pp. 332-343). Springer International Publishing. doi:10.1007/978-3-030-97546-3_27
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, GeoffreyYogarajan, 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
Cassales, G., Gomes, H., Bifet, A., Pfahringer, B., & Senger, H. (2021). Improving the performance of bagging ensembles for data streams through mini-batching. Information Sciences, 580, 260-282. doi:10.1016/j.ins.2021.08.085
Bravo-Marquez, F., Khanchandani, A., & Pfahringer, B. (2021). Incremental word vectors for time-evolving sentiment lexicon induction. Cognitive Computation. doi:10.1007/s12559-021-09831-y
Other publications by:
PFAHRINGER, Bernhard MYogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2021). Transformers for multi-label classification of medical text: an empirical comparison. In A. Tucker, P. Henriques Abreu, J. Cardoso, P. Pereira Rodrigues, & D. Riaño (Eds.), Proc 19th International Conference on Artificial Intelligence in Medicine (AIME 2021), LNCS 12721 (pp. 114-123). Virtual Event: Springer. doi:10.1007/978-3-030-77211-6_12
Open Access version at:
https://hdl.handle.net/10289/14934
Other publications by:
MONTIEL LOPEZ, Jacob :: YOGARAJAN, Vithya :: SMITH, Anthony C (Tony) :: PFAHRINGER, Bernhard MRead, J., Pfahringer, B., Holmes, G., & Frank, E. (2021). Classifier chains: A review and perspectives. Journal of Artificial Intelligence Research, 70, 683-718. doi:10.1613/jair.1.12376
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