Dr Michael Mayo
Senior Lecturer (Computer Science)
Qualifications: PhD Cant BA Hons Otago
Phone: +64 7 838 4403
Fax: +64 7 858 5095
Metaheuristic/evolutionary algorithms, AI, and machine learning, and applications in areas such as data mining and renewable energy.
- 1/05/16: Slides from a talk I gave on Wind Farm Layout Optimisation at WCCI in Vancouver.
- 24/05/16: I am chairing a Special Session on Computation Intelligence in Renewable Energy at the IES 2016 conference. Feel free to submit a paper!
- 18/05/16: An online lesson on image classification using machine learning, which is part of the Advanced Data Mining with Weka MOOC, is available this week. You can also directly access the software for the lesson here.
- 21/04/16: Slides from a presentation on predicting appliance energy usage that I gave at Biologically Inspired Techniques for Data Mining (BDM'16) in Auckland in April 19th
Mayo, M. J., & Omranian, S. (2016). Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators. In H. Cao, J. Li, & R. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining, PAKDD 2016 Workshops, Revised Selected Papers Vol. LNCS 9794 (pp. 3-14). Conference held at Auckland, NZ: Springer. doi:10.1007/978-3-319-42996-0_1 Open Access version: hdl:10289/10560
Mayo, M., & Bifet, A. (2016). Deferral classification of evolving temporal dependent data streams. In Proc 31st Annual ACM Symposium on Applied Computing (pp. 952-954). Pisa, Italy: ACM. doi:10.1145/2851613.2851890 Open Access version: hdl:10289/10549
Mayo, M. J., Daoud, M., & Zheng, C. (2016). Randomising block sizes for BlockCopy-based wind farm layout optimisation. In G. Leu, H. K. Singh, & S. Elsayed (Eds.), Proc 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems Vol. Proceedings in Adaptation, Learning and Optimization 8 (pp. 277-289). Canberra, Australia: Springer. doi:10.1007/978-3-319-49049-6_20 Open Access version: hdl:10289/10779
Find more research publications by Michael Mayo