Mr Peter Reutemann
Senior Research Programmer (Computer Science)
Qualifications: MSc CS Freiburg
Personal Website: http://www.cms.waikato.ac.nz/~fracpete/
About Peter
Open-source advocate, specialized in workflow engines, machine learning and data mining. Lead developer of the ADAMS workflow engine.
Research Interests
Some of the research projects Peter has been involved in are:
- ADAMS (Advanced Data mining and Machine learning System)
- MOA (Massive Online Analysis)
- MEKA: A Multi-label Extension to WEKA
- Weka
- python-weka-wrapper: using Weka from within Pyton (Python 2.7 and Python 3)
- weka-virtualenv: virtual environments for Weka
- Overview of Projects
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: https://hdl.handle.net/10289/10136
Reutemann, P., & Holmes, G. (2015). Big data with ADAMS. In 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications Vol. 41 (pp. 5-8). Conference held Sydney, Australia: JMLR Workshop and Conference Proceedings. Retrieved from http://jmlr.org/proceedings/papers/v41/reutemann15.html
Hill, M. G., Connolly, P. G., Reutemann, P., & Fletcher, D. (2014). The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Computers and Electronics in Agriculture, 108, 250-257. doi:10.1016/j.compag.2014.08.011
Reutemann, P., & Vanschoren, J. (2012). Scientific Workflow Management with ADAMS. In P. Flach, T. De Bie, & N. Cristianini (Eds.), Proceedings of the Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) (pp. 833-837). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33486-3_58
Find more research publications by Peter Reutemann
Contact Details
Email: fracpete@waikato.ac.nzRoom: G.2.19
Phone: +64 7 858 5174