Dr Tony Smith
Senior Lecturer (Computer Science)
Qualifications: MSc Calg PhD Waikato
I received my undergraduate and graduate degrees in Computer Science from the University of Calgary, and earned my PhD at the University of Waikato.
A general theme of my research is adaptive systems. My background is in artificial intelligence and machine learning, with particular focus on language processing problems. More recently I have been spending quite a bit of time working on bioinformatics projects, and on applications of reinforcement learning within autonomous agents.
I am a member of the editorial board for the International Journal on Intelligent Data Analysis, and a charter member of (among other things) the Australasian Language Technology Association.
Yogarajan, V., Gouk, H., Smith, T., Mayo, M., & Pfahringer, B. (2020). Comparing high dimensional word embeddings trained on medical text to bag-of-words for predicting medical codes. In P. Sitek, M. Petranik, M. Krótkiewicz, & C. Srinilta (Eds.), Proc 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020) LNCS 12033 (pp. 97-108). Phuket, Thailand: Springer. doi:10.1007/978-3-030-41964-6_9 Open Access version: https://hdl.handle.net/10289/13591
Peng, M., Sundararajan, V., Williamson, T., Minty, E. P., Smith, T. C., Doktorchik, C. T. A., & Quan, H. (2018). Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes. Data in Brief, 18, 710-712. doi:10.1016/j.dib.2018.02.043 Open Access version: https://hdl.handle.net/10289/11839
Peng, M., Sundararajan, V., Williamson, T., Minty, E. P., Smith, T. C., Doktorchik, C. T. A., & Quan, H. (2018). Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data. Journal of Biomedical Informatics, 79, 41-47. doi:10.1016/j.jbi.2018.02.001 Open Access version: https://hdl.handle.net/10289/12688
Smith, T. C., & Frank, E. (2016). Introducing machine learning concepts with WEKA. In E. Mathé, & S. Davis (Eds.), Statistical Genomics: Methods and Protocols (Vol. 1418, pp. 353-378). Springer. doi:10.1007/978-1-4939-3578-9_17
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