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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

Find more research publications by Bernhard Pfahringer