Professor Eibe Frank
Professor (Computer Science)
Qualifications: Dipl-Inf Karlsruhe PhD Waikato
Personal Website: http://www.cs.waikato.ac.nz/~eibe/
Research Interests
Professor Frank is a computer scientist whose primary area of interest is machine learning and its applications. He is a core developer of the WEKA machine learning software and has more than 100 publications on machine learning methods and their application to data mining, text mining, and areas of research outside computer science.
Recent Publications
Montiel, J., Mitchell, R., Frank, E., Pfahringer, B., Abdessalem, T., & Bifet, A. (2020). Adaptive XGBoost for evolving data streams. In Proc 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Glasgow, UK: IEEE. doi:10.1109/IJCNN48605.2020.9207555
Mayo, M., & Frank, E. (2020). Improving naive Bayes for regression with optimised artificial surrogate data. Applied Artificial Intelligence, 34(6), 484--514. doi:10.1080/08839514.2020.1726615 Open Access version: https://hdl.handle.net/10289/12514
Sahito, A., Frank, E., & Pfahringer, B. (2020). Transfer of pretrained model weights substantially improves semi-supervised image classification. In M. Gallagher, N. Noustafa, & E. Lakshika (Eds.), AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science Vol. LNAI 12576 (pp. 434-444). Cham: Springer. doi:10.1007/978-3-030-64984-5_34
Wang, H., Gouk, H., Frank, E., Pfahringer, B., & Mayo, M. (2020). A comparison of machine learning methods for cross-domain few-shot learning. In M. Gallagher, N. Moustafa, & E. Lakshika (Eds.), AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science Vol. LNAI 12576 (pp. 445-457). Cham: Springer. doi:10.1007/978-3-030-64984-5_35 Open Access version: https://hdl.handle.net/10289/14027
Find more research publications by Eibe Frank
Keywords
Data mining; Machine Learning
Contact Details
| Name | Extn. | Username | Room | Department | ||||
|---|---|---|---|---|---|---|---|---|
| Frank, Prof Eibe | 4396 | eibe | G.2.18 | Computer Science |
You can contact staff by:
- Calling +64 7 838 4466 select option 1, then enter the extension
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