Professor Bernhard Pfahringer
Professor (Computer Science)
Qualifications: MEng PhD Vienna Tech
Contact Details
Email: bernhard@waikato.ac.nz
Room: G.1.24
Phone: +64 7 838 4041
Extension: 4041
Fax: +64 7 858 5095
Website: http://www.cs.waikato.ac.nz/~bernhard/
About Bernhard
Find out more about Bernhard and his work by viewing his professorial lecture: Dawn of the Age of Data: a Lecture to look at the data revolution (October 2015).
Research Interests
Data mining, machine learning, heuristic optimisation, programming languages.
Teaching Commitments
Recent Publications
Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Merit-guided dynamic feature selection filter for data streams. Expert Systems with Applications, 116, 227-242. doi:10.1016/j.eswa.2018.09.031
Gouk, H., Pfahringer, B., Frank, E., & Cree, M. (2019). MaxGain: Regularisation of neural networks by constraining activation magnitudes. In M. Berlingerio, F. Bonchi, T. Gärtner, N. Hurley, & G. Ifrim (Eds.), Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science Vol. 11051 (pp. 541-556). Cham: Springer. doi:10.1007/978-3-030-10925-7_33 Open Access version: https://hdl.handle.net/10289/12301
Bravo Marquez, F., Frank, E., & Pfahringer, B. (2018). Transferring sentiment knowledge between words and tweets. Web Intelligence, 16(4), 203-220. doi:10.3233/WEB-180389 Open Access version: https://hdl.handle.net/10289/12209
Yuan, L., Pfahringer, B., & Barddal, J. P. (2018). Iterative subset selection for feature drifting data streams. In Proc 33rd Annual ACM Symposium on Applied Computing (SAC 2018) (pp. 510-517). New York, NY: ACM. doi:10.1145/3167132.3167188