Research Publications for Geoffrey Holmes

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Author's Publications

Publications ByHOLMES, Geoffrey

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  • Gibert, K., Horsburgh, J. S., Athanasiadis, I. N., & Holmes, G. (2018). Environmental data science. Environmental Modelling & Software, 106, 4-12. doi:10.1016/j.envsoft.2018.04.005

  • Chen, Y., Zhou, Y., Sharifi, N., Murch, R., & Holmes, G. (2018). Drug delivery via nanomachines. In X. Shen, X. Lin, & K. Zhang (Eds.), Encyclopedia of Wireless Networks (pp. 1-7). Cham: Springer. doi:10.1007/978-3-319-32903-1_224-1

  • Chen, Y., Sharifi, N., & Holmes, G. (2018). Biosensing by learning: cancer detection as iterative optimization. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18). Conference held at Honolulu, Hawaii: IEEE.

  • Bifet, A., Read, J., Holmes, G., & Pfahringer, B. (2018). Streaming data mining with Massive Online Analytics (MOA). In M. Last, H. Bunke, & A. Kandel (Eds.), Data Mining in Time Series and Streaming Databases (pp. 1-25).

  • Bifet, A., Gavaldà, R., Pfahringer, B., & Holmes, G. (2018). Machine learning for data streams with practical examples in MOA. MIT Press. Retrieved from https://mitpress.mit.edu/books/machine-learning-data-streams

  • Gibert, K., Horsburgh, J. S., Athanasiadis, I. N., & Holmes, G. (2018). Preface to the thematic issue on Environmental Data Science. Applications to air quality and water cycle. Environmental Modelling and Software, 106, 1-3. doi:10.1016/j.envsoft.2018.03.020

  • van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2017). The online performance estimation framework: heterogeneous ensemble learning for data streams. Machine Learning. [First online: 2017], 107(1), 149-176. doi:10.1007/s10994-017-5686-9

  • 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

  • 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 http://proceedings.mlr.press/v77/leathart17a.html

  • Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., . . . Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, (Online First), 27 pages. doi:10.1007/s10994-017-5642-8

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