Projects per year
Personal profile
Academic Background
My principal research interests lie in machine learning, with a particular emphasis on Bayesian and kernel learning methods. I am most interested in theoretical issues and algorithms with a direct impact in the practical application of machine learning techniques, including topics such as feature selection, model selection, performance estimation, model comparison, covariate shift, dealing with imbalanced or "non-standard" data and semi-supervised learning. Most of my applied work centres on problems arising in computational biology, in collaboration with the School of Chemistry and Pharmacy (CAP) and with the nearby John Inness Centre (JIC) and Institute for Food Research (IFR). However I also have long-standing research links with the School of Environmental Sciences (ENV) and the Climatic Research Unit (CRU), working on applications of machine learning in the environmental sciences, particularly on modelling and exploiting predictive uncertainty.
Biography
Website: http://theoval.cmp.uea.ac.uk/~gcc/
Follow this link for details of current PhD opportunities in Computing Sciences. But feel free to email me to discuss projects outside these areas and alternative sources of funding.
External Activities
- MRC Discipline-hopping Fellowship, 2004
- Joint Editor, Special Issue of Neurocomputing, 2003 and 2004
- Co-chair Multi-level Optimisation Workshop at NIPS-2006
- Co-chaired the workshop on Agnostic Learning versus Prior Knowledge at IJCNN-2007
Key Research Interests and Expertise
Gavin Cawley is part of the Computational Biology Group and the Knowledge Discovery and Data Mining Group
Gavin's current research interests include a continuation of his post-graduate research on neural networks in speech synthesis, and classification of atmospheric circulation patterns (also using neural networks), in collaboration with Dr Steve Dorling.
Selected Publications:
Saadi, K., Talbot, N.L.C., and Cawley, G.C. Optimally regularised kernel Fisher discriminant classification. Neural Networks, Volume 20, Issue 7, Page(s) 832-841, 2007.
Cawley, G. C. and Talbot, N. L. C. Preventing over-fitting during model selection using Bayesian regularisation. Journal of Machine Learning Research, Volume 8, Page(s) 841-861, 2007.
Cawley, G. C. and Talbot, N. L. C. Gene selection in cancer classification using sparse logistic regression with Bayesian regularisation. Bioinformatics, Volume 22, Number 19, Page(s) 2348-2355, 2006.
Cawley, G. C. and Talbot, N. L. C. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognition, Volume 36, Issue 11, Page(s) 2585-2592, 2003.
Key Responsibilities
Chair of Board of Examiners (Postgraduate Teaching)
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Smart Environments Research Facility
Lettice, F., Aung, M. H., Bagnall, T., Buckley, O., Cawley, G., Day, A., De La Iglesia, B., Finlayson, G., Harvey, R., Huber, K., Kulinskaya, E., Laycock, S., Lines, J., Mackiewicz, M., Milner, B., Moulton, V., Parr, G., Ren, E. & Wang, W.
Engineering and Physical Sciences Research Council
10/01/20 → 8/01/22
Project: Research
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Advancing Machine Learning Methodology for New Classes of Prediction Problems
Cawley, G., Hayward, S. & Moore, G.
Engineering and Physical Sciences Research Council
25/02/08 → 24/08/11
Project: Research
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Nested cross-validation when selecting classifiers is overzealous for most practical applications
Wainer, J. & Cawley, G., 17 May 2021, In : Expert Systems with Applications. 182, 115222.Research output: Contribution to journal › Article
1 Citation (Scopus) -
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
Middlehurst, M., Large, J., Cawley, G. & Bagnall, A., 25 Feb 2021, p. 660-676. 17 p.Research output: Contribution to conference › Paper
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Investigation of sequence features of hinge-bending regions in proteins with domain movements using kernel logistic regression
Veevers, R., Cawley, G. & Hayward, S., 9 Apr 2020, In : BMC Bioinformatics. 21, 137.Research output: Contribution to journal › Article
Open AccessFile19 Downloads (Pure) -
Empirical evaluation of resampling procedures for optimising SVM hyperparameters
Wainer, J. & Cawley, G., 1 Feb 2017, In : Journal of Machine Learning Research. 18, 15, p. 1-35 35 p.Research output: Contribution to journal › Article
Open AccessFile22 Citations (Scopus)10 Downloads (Pure) -
Over-Fitting in Model Selection with Gaussian Process Regression
Mohammed, R. O. & Cawley, G. C., 15 Jul 2017, Machine Learning and Data Mining in Pattern Recognition: 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings. Perner, P. (ed.). 1 ed. Springer, Vol. 10358. p. 192-205 14 p. (Lecture Notes in Computer Science)(Lecture Notes in Artificial Intelligence).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile10 Citations (Scopus)33 Downloads (Pure)