Dr Patricia Jean Riddle

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Senior Lecturer

Research | Current

My main research interests are in the AI areas of machine learning and datamining.

In particular, I am interested in various techniques for machine learning (such as ensemble approaches, techniques which overcome overfitting problems, and data-engineering as incorporating background knowledge) and their applications to real world problems. In additon I have been working in the area of search, planning, and representation increasingly in the last few years.

Find more information on my personal website.

Selected publications and creative works (Research Outputs)

  • Benavides-Prado, D., Koh, Y., & Riddle, P. (2017). AccGenSVM: Selectively transferring from previous hypotheses. In C. Sierra (Ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 1440-1446. Melbourne, Australia.
    Other University of Auckland co-authors: Yun Sing Koh, Diana Benavides Prado
  • Peng, A. Y., Koh, Y. S., & Riddle, P. (2017). mHUIMiner: A fast high utility itemset mining algorithm for sparse datasets. In J. Kim, K. Shim, L. Cao, J. G. Lee, X. Lin, Y. S. Moon (Eds.) Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017 (Lecture Notes in Computer Science), 10235 (Part 2), 196-207. Jeju, South Korea. 10.1007/978-3-319-57529-2_16
    URL: http://hdl.handle.net/2292/38686
    Other University of Auckland co-authors: Yun Sing Koh, Alex Peng
  • Versteegen, R., Gimel’farb G, & Riddle, P. (2016). Texture modelling with nested high-order Markov–Gibbs random fields. Computer Vision and Image Understanding, 143, 120-134. 10.1016/j.cviu.2015.11.003
    URL: http://hdl.handle.net/2292/30306
    Other University of Auckland co-authors: Georgy Gimel'farb
  • Versteegen, R., Gimel'farb G, & Riddle, P. (2016). Markov-Gibbs Texture Modelling with Learnt Freeform Filters. Paper presented at Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR) and Statistical Techniques in Pattern Recognition (SPR), Merida, MEXICO. 29 November - 2 December 2016. STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, S+SSPR 2016. (pp. 11). 10.1007/978-3-319-49055-7_34
    Other University of Auckland co-authors: Georgy Gimel'farb
  • Chen, K., Koh, Y. S., & Riddle, P. (2016). Proactive drift detection: Predicting concept drifts in data streams using probabilistic networks. Proceedings of the International Joint Conference on Neural Networks, 780-787. Vancouver, Canada: IEEE. 10.1109/IJCNN.2016.7727279
    URL: http://hdl.handle.net/2292/41721
    Other University of Auckland co-authors: Kylie Chen, Yun Sing Koh
  • Riddle, P. J., Barley, M., Franco, S., & Douglas, J. (2015). Automated transformation of PDDL representations. In L. Lelis, R. Stern (Eds.) Proceedings of the Eighth Annual Symposium on Combinatorial Search, SOCS 2015, 11-13 June 2015, Ein Gedi, the Dead Sea, Israel, 214-215. Ein Gedi, the Dead Sea, Israel: AAAI PRESS.
    Other University of Auckland co-authors: Michael Barley, Jordan Douglas
  • Chen, K., Koh, Y. S., & Riddle, P. (2015). Tracking Drift Severity in Data Streams. Paper presented at 28th Australasian Joint Conference on Artificial Intelligence (AI), Canberra, AUSTRALIA. 30 November - 4 December 2015. AI 2015: ADVANCES IN ARTIFICIAL INTELLIGENCE. (pp. 13). 10.1007/978-3-319-26350-2_9
    Other University of Auckland co-authors: Kylie Chen, Yun Sing Koh
  • Versteegen, R., Gimel'farb G, & Riddle, P. J. (2014). Learning High-order Generative Texture Models. IVCNZ '14 Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, 90-95. Hamilton, New Zealand. 10.1145/2683405.2683420
    URL: http://hdl.handle.net/2292/24923
    Other University of Auckland co-authors: Georgy Gimel'farb

Contact details

Primary office location

SCIENCE CENTRE 303S - Bldg 303S
Level 4, Room 490
38 PRINCES ST
AUCKLAND 1010
New Zealand

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