Professor Bernhard Pfahringer

Professor (Computer Science)

Qualifications: MEng PhD Vienna Tech

Contact Details

Room: G.1.24
Phone: +64 7 838 4041
Extension: 4041
Fax: +64 7 858 5095

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

  • Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfahringer, B., . . . Abdessalem, T. (2019). Correction to: Adaptive random forests for evolving data stream classification (Machine Learning, (2017), 106, 9-10, (1469-1495), 10.1007/s10994-017-5642-8). Machine Learning. doi:10.1007/s10994-019-05793-3

  • Peng, A. Y., Sing Koh, Y., Riddle, P., & Pfahringer, B. (2019). Using supervised pretraining to improve generalization of neural networks on binary classification problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11051 LNAI (pp. 410-425). doi:10.1007/978-3-030-10925-7_25

  • 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:

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