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

  • Holmes, G., Liu, T. Y., Li, H., King, I., Sugiyama, M., & Zhou, Z. H. (2017). Introduction: Special Issue of Selected Papers from ACML 2015. Machine Learning, 106(4), 459-461. doi:10.1007/s10994-017-5636-6 Open Access version:

  • Durrant, R. J., Kim, K. E., Holmes, G., Marsland, S., Sugiyama, M., & Zhou, Z. H. (2017). Foreword: Special issue for the Journal Track of the 8th Asian Conference on Machine Learning (ACML 2016). Machine Learning, 106(5), 623-625. doi:10.1007/s10994-017-5637-5 Open Access version:

  • Leathart, T., Frank, E., Holmes, G., & Pfahringer, B. (2017). Probability calibration trees. In M. -L. Zhang, & Y. -K. Noh (Eds.), Proc 9th Asian Conference on Machine Learning (ACML 2017) Vol. PMLR 77 (pp. 145-160). Seoul, Korea. Retrieved from Open Access version:

  • Bifet, A., Zhang, J., Fan, W., He, C., Zhang, J., Qian, J., . . . Pfahringer, B. (2017). Extremely fast decision tree mining for evolving data streams. In Proc 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1733-1742). Conference held Halifax, NX, Canada: ACM. doi:10.1145/3097983.3098139 Open Access version:

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