Professor Eibe Frank
Professor (Computer Science)
Qualifications: Dipl-Inf Karlsruhe PhD Waikato
Contact Details
Email: eibe@waikato.ac.nz
Room: G.2.18
Phone: +64 7 838 4396
Extension: 4396
Fax: +64 7 858 5095
Website: http://www.cs.waikato.ac.nz/~eibe/
Research Interests
Professor Frank is a computer scientist whose primary area of interest is machine learning and its applications. He is a core developer of the WEKA machine learning software and has more than 100 publications on machine learning methods and their application to data mining, text mining, and areas of research outside computer science.
Recent Publications
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: https://hdl.handle.net/10289/12301
Vetrova, V., Coup, S., Frank, E., & Cree, M. J. (2018). Difference in details: transfer learning case study of cryptic plants and moths. In 5th Workshop on Fine-Grained Visual Categorization held in conjunction with CVPR 2018. Conference Website. Open Access version: https://hdl.handle.net/10289/12006
Vetrova, V., Coup, S., Frank, E., & Cree, M. J. (2018). Hidden features: experiments with feature transfer for fine-grained multi-class and one-class image categorization. In Proc 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 6 pages). Auckland, NZ. doi:10.1109/IVCNZ.2018.8634790
Gurulian, I., Markantonakis, K., Frank, E., & Akram, R. N. (2018). Good vibrations: artificial ambience-based relay attack detection. In Proc 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 481-489). Los Alamitos, California: IEEE Computer Society. doi:10.1109/TrustCom/BigDataSE.2018.00075 Open Access version: https://hdl.handle.net/10289/12322
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