Professor Geoff Holmes

Dean of Computing & Mathematical Sciences

Qualifications: BSc(Hons) PhD S'ton

Contact Details

Room: FG.1.01
Phone: +64 7 838 4405
Extension: 4405
Fax: +64 7 838 4155

About Geoff

I received my degrees in Mathematics from the University of Southampton, England. My PhD involved the development of software packages to assist Mathematicians in solving Einstein's field equations in General Relativity. This was how I got started in Computer Science. After graduating I became a Research Assistant at the Electrical Engineering Department of Cambridge University, England where I was a member of large team working on a speech understanding system. I took up a position as Lecturer in Computer Science in 1987. In 1993 I was appointed Senior Lecturer.

Research Interests

My research interests are fairly broad. I have always held an interest in Computer Speech and have supervised several projects at Waikato on that topic, in particular, speech recognition and speech compression. I currently have two PhD students working on speech compression. I am part of the Department's Machine Learning group where I concentrate my efforts on the application of Machine Learning to agricultural domains. Through my interests in Machine Learning I have recently become very interested in the concept of knowledge discovery in databases.

Recent Publications

  • Read, J., Reutemann, P., Pfahringer, B., & Holmes, G. (2016). MEKA: A multi-label/multi-target extension to WEKA. Journal of Machine Learning Research, 17(21), 1-5. Open Access version: hdl:10289/10136

  • van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2015). Case study on bagging stable classifiers for data streams. In Twenty-fourth Belgian-Dutch Conference on Machine Learning. Delft, Netherlands. Open Access version: hdl:10289/9765

  • Bifet, A., de Francisci Morales, G., Read, J., Holmes, G., & Pfahringer, B. (2015). Efficient online evaluation of big data stream classifiers. In Proc 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 59-68). Sydney, Australia: ACM. doi:10.1145/2783258.2783372 Open Access version: hdl:10289/10145

  • van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2015). Having a Blast: meta-learning and heterogeneous ensembles for data streams. In Proc IEEE International Conference on Data Mining (pp. 1003-1008). Atlantic City, USA: IEEE. doi:10.1109/ICDM.2015.55

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