UEA Computational Biology Laboratory

2021

• J. Wainer and G. Cawley, "Nested cross-validation when selecting classifiers is overzealous for most practical applications", Expert Systems with Applications, vol. 182, 115222, 2021. (doi:j.eswa.2021.115222)
  
  
• Matthew Middlehurst, James Large, Gavin Cawley and Anthony Bagnall, "The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification", in ECML PKDD 2020: Machine Learning and Knowledge Discovery in Databases, ed. Frank Hutter, Kristian Kersting, Jefrey Lijffijt and Isabel Valera, pages 660-676, Lecture Notes in Computer Science, Springer International Publishing, 2021. (doi:10.1007/978-3-030-67658-2_38, arXiv:2105.03841)

2020

• Veevers, R., Cawley, G. & Hayward, S., "Investigation of sequence features of hinge-bending regions in proteins with domain movements using kernel logistic regression", BMC Bioinformatics, vol. 21, issue 137, 2020. (doi:10.1186/s12859-020-3464-3)

2018

• A. Bagnall, M. Flynn, J. Large, J. Lines, A. Bostrom and G. Cawley, "Is rotation forest the best classifier for problems with continuous features?", arXiv preprint, 2018. (arXiv:1809.06705)

2017

• Rekar O. Mohammed and Gavin C. Cawley, "Over-Fitting in Model Selection with Gaussian Process Regression", in Proceedings of the International Conference on Machine Learning and Data Mining in Pattern Recognition", Petra Perner (ed), pages 192-205, Springer International Publishing", ISBN 978-3-319-62416-7, 2017. (doi:10.1007/978-3-319-62416-7_14)
  
  
• Anthony Bagnall and Gavin C. Cawley, "On the use of default parameter settings in the empirical evaluation of classification algorithms", arXiv preprint, 2017. (arXiv:1703.06777)
  
  
• Jacques Wainer and Gavin Cawley, "Empirical evaluation of resampling procedures for optimising SVM hyperparameters", Journal of Machine Learning Research 18 (15), 1-35 2017. (pdf)

2015

• Awat Saeed, Gavin C. Cawley, and Anthony Bagnall, “Benchmarking the Semi-Supervised Naive Bayes Classifier”, IEEE/INNS International Joint Conference on Artificial Neural Networks (IJCNN), Killarney, Ireland, 2015.
  (doi:10.1109/IJCNN.2015.7280665)
  
  
• Isabelle Guyon, Kristin Bennett, Gavin Cawley, Hugo Jair Escalante, Sergio Escalera, Tin Kam Ho, Nuria Macia, Bisakha Ray, Alexander Statnikov and Evelyne Viegas, “Design of the 2015 ChaLearn AutoML Challenge”, IEEE/INNS International Joint Conference on Artificial Neural Networks (IJCNN), Killarney, Ireland, 2015.
  (doi:10.1109/IJCNN.2015.7280767).
  
  
• Gavin C. Cawley, Kevin Cowtan, Robert G. Way, Peter Jacobs, Ari Jokimäki, "On a minimal model for estimating climate sensitivity", Ecological Modelling, Volume 297, Pages 20-25, ISSN 0304-3800, 10 February 2015.
  (doi:10.1016/j.ecolmodel.2014.10.018)

2014

• Daniel Taylor, Gavin Cawley and Steven Hayward, "Quantative Method for the Assignment of Hinge and Shear Mechansim in Protein Domain Movements", Bioinformatics, July 2014. (doi:10.1093/bioinformatics/btu506)
  
  
• Gavin C. Cawley and Nicola L. C. Talbot, "Kernel Learning at the First Level of Inference", Neural Networks, volume 53, pages 69-80, May 2014. (doi:10.1016/j.neunet.2014.01.011, preprint)

2013

• Daniel Taylor, Gavin Cawley and Steven Hayward, "Classification of Domain Movements in Proteins Using Dynamic Contact Graphs", PLOS One, volume 8, number 11, e81224, November 18, 2013. (doi:10.1371/journal.pone.0081224)

2012

• Gavin C. Cawley, "Over-Fitting in Model Selection and Its Avoidance", in Advances in Intelligent Data Analysis XI (IDA 2012), Lecture Notes in Computer Science Jaakko Hollmén, Frank Klawonn and Allan Tucker (eds), Springer, Berlin Heidelberg, ISBN 978-3-642-34156-4, 2012. (doi:10.1007/978-3-642-34156-4_1,slides)

2011

• Guang Lan Zhang, Hifzur Rahman Ansari, Phil Bradley, Gavin C. Cawley, Tomer Hertz, Xihao Hu, Nebojsa Jojic, Yohan Kim, Oliver Kohlbacher, Ole Lund, Claus Lundegaard, Craig A. Magaret, Morten Nielsen, Harris Papadopoulos, G.P.S. Raghava, Vider-Shalit Tal, Li C. Xue, Chen Yanover, Shanfeng Zhu, Michael T. Rock, James E. Crowe Jr., Christos Panayiotou, Marios M. Polycarpou, Włodzisław Duch and Vladimir Brusic, Machine learning competition in immunology – Prediction of HLA class I binding peptides", Journal of Immunological Methods, volume 374, number 1-2, pages 1-4, November 2011, (www)
  
  
• Gavin C. Cawley, On the atmospheric residence time of anthropogenically sourced carbon dioxide, Energy & Fuels, volume 25, number 11, pages 5503–5513, September 2011. (pdf,www)
  
  
• Gavin C. Cawley, Steven Hayward, Gareth Janacek and Geoff Moore, Sparse Bayesian prediction of disordered residues and disordered regions based on amino-acid composition, in Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), pages 1618-1623, San Jose, CA, July 31 2011-Aug. 5 2011. (www)
  
  
• Gavin C. Cawley, Baseline Methods for Active Learning, Journal of Machine Learning Research - Workshop and Conference Proceedings, volume 16, pages 47-57, 2011. (www)
  
  
• I. Guyon, G. Cawley, G. Dror and V. Lemaire, Results of the active learning challenge, Journal of Machine Learning Research - Workshop and Conference Proceedings, volume 16, pages 19-45, 2011. (www)

2010

• G. C. Cawley and G. J. Janacek, Letter: On allometric equations for predicting body mass of dinosaurs", Journal of Zoology, volume 282, issue 4, pages 223–225, December 2010. (www)
  
  
• G. C. Cawley, Causal and non-causal feature selection for ridge regression, in "Causation and Prediction Challenge", Challenges in Machine Learning, volume 2, Microtome Publishing, ISBN 978-0971977723, 2010
  
  
• G. C. Cawley and N. L. C. Talbot, Over-fitting in model selection and subsequent selection bias in performance evaluation, Journal of Machine Learning Research, 2010. Research, vol. 11, pp. 2079-2107, July 2010. (www, pdf)
  
  
• I. Guyon, G. Cawley, G. Dror and V. Lemaire, Design and Analysis of the WCCI 2010 Active Learning Challenge, Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2010) (accepted), Barcelona, Spain, July 18-23, 2010
  
  
• G. C. Cawley and G. J. Janacek, On allometric equations for predicting body mass of dinosaurs, Journal of Zoology, vol. 280, no. 4, pages 355-361, April 2010. (doi, pdf,supplementary material)
  
  
• I. Guyon, A. Saffari, G. Dror and G. Cawley, Model selection: Beyond the Bayesian/frequentist divide, Journal of Machine Learning Research, vol. 11, pp. 61-87, 2010. (pdf)

2009

• G. C. Cawley, Causal & non-causal feature selection for ridge regression, Journal of Machine Learning Research: Workshop and Conference Proceedings, volume 3: Causation and Prediction Challenge (WCCI 2008), pp. 107-128, 2009. (pdf)

2008

• G. C. Cawley and N. L. C. Talbot, Efficient approximate leave-one-out cross-validation for kernel logistic regression, Machine Learning, vol, 71, no. 2-3, pp. 243--264, June 2008. (doi,pdf)
  
  
• Guyon, I., Saffari, A., Dror, G. and Cawley, G., Analysis of the IJCNN 2007 agnostic learning versus prior knowledge challenge, Neural Networks, vol. 21, issues 2-3, pp. 544-550, March-April 2008. (doi)
  
  
• B.-J. Theobald, G. Cawley, A. Bangham, I. Matthews and N. Wilkinson, Comparing text-driven and speech-driven visual speech synthesisers, In INTERSPEECH-2008, page 2322, 2008.

2007

• K. Saadi, N. L. C. Talbot, and G. C. Cawley, Optimally regularised kernel Fisher discriminant classification, in Neural Networks, volume 20, number 7, pages 832-841, September 2007. (doi,pdf)
  
  
• G. C. Cawley, G. J. Janacek and N. L. C. Talbot, Generalised kernel machines, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007), pages 1732-1737, Orlando, Florida, USA, August 12-17, 2007. (doi,pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, Agnostic learning versus prior knowledge in the design of kernel machines, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007), pages 1720-1725, Orlando, Florida, USA, August 12-17, 2007. (doi,pdf)
  
  
• I. Guyon, A. Saffari, G. Dror and G. Cawley, Agnostic learning vs. prior knowledge challenge, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007), pages 829-834, Orlando, Florida, USA, August 12-17, 2007. (doi)
  
  
• G. C. Cawley, G. J. Janacek, M. R. Heylock and S. R. Dorling, Predictive uncertainty in environmental modelling, Neural Networks, vol. 20, issue 4, pages 537-549, May 2007. (doi,pdf)
  
  
• G. C. Cawley, Model selection for kernel probit regression, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2007), pages 217-222, Bruges, Belgium, April 25-27, 2007. (pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, Preventing over-fitting in model selection via Bayesian regularisation of the hyper-parameters, Journal of Machine Learning Research, volume 8, pages 841-861, April 2007. (www,pdf,supplementary material)
  
  
• G. C. Cawley, N. L. C. Talbot and M. Girolami, Sparse multinomial logistic regression via Bayesian L1 regularisation, In Advances in Neural Information Processing Systems 19, B. Schölkopf, J. C. Platt and T. Hoffmann (eds), pages 209-216, MIT Press, Cambridge MA USA, 2007. (pdf,software)
  
  
• A. Bagnall, G. Cawley, I. Whittley, L. Bull, M. Studley, M. Pettipher and F. Tekiner, Super computer hetrogenous classifier meta-ensembles, International Journal of Data Warehousing and Mining, volume 3, number 2, pages 67-82, April-June 2007.

2006

• G. C. Cawley and N. L. C. Talbot, Gene selection in cancer classification using sparse logistic regression with Bayesian regularisation, Bioinformatics, volume 22, number 19, pages 2348-2355, October 2006. (doi,pdf,software)
  
  
• D. J. Bescoby, G. C. Cawley and P. N. Chroston, Enhanced interpretation of magnetic survey data from archaeological sites using artificial neural networks, Geophysics, vol. 71, issue 5, pages H45-H53, September/October 2006. (doi)
  
  
• M. R. Haylock, G. C. Cawley, C. Harpham, R. Wilby and C. M. Goodess, Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios, International Journal of Climatology, volume 26, issue 10, pp. 1397-1415, August 2006. (doi)
  
  
• G. C. Cawley, Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs, In Proceedings of the International Joint Conference on Neural Networks (IJCNN-2006), pages 1661-1668, Vancouver, BC, Canada, July 16-21 2006. (pdf)
  
  
• G. C. Cawley, M. R. Haylock and S. R. Dorling, Predictive Uncertainty in Environmental Modelling, In Proceedings of the International Joint Conference on Neural Networks (IJCNN-2006), pages 5347-5354, Vancouver, BC, Canada, July 16-21 2006. (pdf)
  
  
• U. Schlink, O. Herbarth, M. Richter, S. Dorling, G. Nunnari, G. Cawley and E. Pelikan, Statistical models to assess the health effects and to forecast ground-level ozone, Environmental Modelling and Software, volume 21, issue 4, pp. 547-558, April 2006. (doi)
  
  
• G. C. Cawley, N. L. C. Talbot, G. J. Janacek and M. W. Peck, Sparse Bayesian kernel survival analysis for modelling the growth domain of microbial pathogens, IEEE Transactions on Neural Networks, volume 17, number 2, pp. 471-481, March 2006. (doi)
  
  
• Y. Li, K. K. Lee, S. Walsh, C. Smith, S. Hadingham, K. Sorefan, G. Cawley and M. W. Bevan, Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine, Genome Research, vol. 16, number 3, pp. 414-427, March 2006. (doi)

2005

• G. C. Cawley and N. L. C. Talbot, Constructing Bayesian formulations of sparse kernel learning methods, Neural Networks , vol. 18, issues 5-6, pp. 674-683, July-August 2005. (doi)
  
  
• G. C. Cawley and N. L. C. Talbot, The evidence framework applied to sparse kernel logistic regression, Neurocomputing, vol. 64, pp. 119-135, 2005. (pdf, doi)
  
  
• J. J. Steil, G. C. Cawley and T. Villmann, Trends in Neurocomputing at ESANN 2004, Neurocomputing, vol. 64, pp 1-4, 2005. (doi)
  
  
• G. C. Cawley, N. L. C. Talbot and O. Chapelle, Estimating Predictive Variances with Kernel Ridge Regression, in Machine Learning Challenges : Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment - First PASCAL Machine Learning Challenges Workshop (MLCW 2005) Revised Selected Papers, Springer Lecture Notes in Artificial Intelligence, LNAI 3944, pp 56-77, Southampton, UK, April 11-13, 2005. (doi)
  
  
• K. Saadi, K.-K. Lee, G. C. Cawley and M. W. Bevan, Predicting sugar regulation in Arabidopsis thaliana using kernel learning methods, In Proceedings of the International Joint Conference on Neural Networks (IJCNN-2005), pp. 167-172, Montreal, Canada, July 31 - August 4 2005. (pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, Sparse Bayesian learning and the relevance multi-layer perceptron network, In Proceedings of the International Joint Conference on Neural Networks (IJCNN-2005) pp. 1320-1324, Montreal, Canada, July 31 - August 4 2005. (pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, A simple trick for constructing Bayesian formulations of sparse kernel learning methods, In Proceedings of the International Joint Conference on Neural Networks (IJCNN-2005), pp. 1425-1430, Montreal, Canada, July 31 - August 4 2005. (pdf)
  
  
• K.-K. Lee, G. C. Cawley and M. W. Bevan, Sparse Bayesian promoter based gene classification, In Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2005), pp 527-532, Bruges, Belgium, April 27-29 2005. (pdf)
  
  

2004

• G. C. Cawley and N. L. C. Talbot, Fast exact leave-one-out cross-validation of sparse least-squares support vector machines, Neural Networks, vol. 17, no. 10, pp 1467-1475, December 2004. (pdf, doi, PubMed)
  
  
• D. Bescoby, G. C. Cawley and N. Chroston, Enhanced interpretation of magnetic survey data using artificial neural networks : A case study from Butrint, Southern Albania, Archaeological Prospection, vol. 11, no. 4, pp 189-199, October/December 2004. (doi)
  
  
• B. J. Theobald, J. A. Bangham, I. A. Matthews and G. C. Cawley, Near-videorealistic synthetic talking faces: implementation and evaluation, Speech Communication, vol. 44, no. 1-4, pp 127-140, October 2004. (doi)
  
  
• G. Nunnari, S. R. Dorling, U. Schlink, G. Cawley, R. Foxall and T. Chatterton, Modelling SO2 concentration at a point with statistical approaches, Environmental Modelling and Software, vol.19, no. 10, pp 887-905, October 2004. (doi)
  
  
• G. C. Cawley, N. L. C. Talbot, R. J. Foxall, S. R. Dorling and D. P. Mandic, Heteroscedastic kernel ridge regression, Neurocomputing, vol. 57, pp 105-124, March 2004. (pdf, doi, MATLAB demo)
  
  
• G. C. Cawley, N. L. C. Talbot, G. J. Janacek and M. W. Peck, Bayesian kernel learning methods for parametric accelerated life survival analysis, In Deterministic and Statistical Methods in Machine Learning: First International Workshop, Lecture Notes in Computer Science, vol. 3635, pp. 37-55, Sheffield, United Kingdom, September 7-10 2004. (doi)
  
  
• K. Saadi, N. L. C. Talbot and G. C. Cawley, Optimally regularised kernel Fisher discriminant analysis, in Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004), vol. 2, pp 427-430, Cambridge, United Kingdom, August 23-26 2004. (pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, Efficient model selection for kernel logistic regression, in Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004), vol. 2, pp 427-430, Cambridge, United Kingdom, August 23-26 2004. (pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, Sparse Bayesian kernel logistic regression, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2004), pp 133-138, Bruges, Belgium, April 28-30, 2004. (pdf)

2003

• B. J. Theobald, S. M. Kruse, J. A. Bangham and G. C. Cawley, Towards a low bandwidth talking face using appearance models, Image and Vision Computing, vol. 21, no. 13-14, pp 1117-1124, December 2003. (doi)
  
  
• G. C. Cawley and N. L. C. Talbot, Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers, Pattern Recognition, vol. 36,no. 11, pp 2585-2592, November 2003. (pdf, doi)
  
  
• J. Kukkonen, L. Partanen, A. Karppinen, J. Ruuskanen, H. Junninen, M. Kolehmainen, H. Niska, S. Dorling, T. Chatterton, R. Foxall and G. Cawley, Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki, Atmospheric Environment, vol. 37, no. 32, pp 4539-4550, October 2003. (doi)
  
  
• S. R. Dorling, R. J. Foxall, D. P. Mandic and G. C. Cawley, Maximum likelihood cost functions for neural network models of air quality data, Atmospheric Environment, vol. 37, no. 24, pp 3435-3443, August 2003. (pdf, doi)
  
  
• U. Schlink, S. Dorling, E. Pelikan, G. Nunnari, G. Cawley, H. Junninen, A. Greig, R. Foxall, K. Eben, T. Chatterton, J. Vondracek, M. Richter, M. Dostal, L. Bertucco, M. Kolehmainen and M. Doyle, A rigorous inter-comparison of ground-level ozone predictions, Atmospheric Environment, vol 37, no. 23, pp 3237-3253, July 2003. (doi)
  
  
• B. Theobald, G. Cawley, J. Glauert, J. A. Bangham and I. Matthews, 2.5d visual speech synthesis using appearance models, in Proceedings of the British Machine Vision Conference (BMVC-2003), vol. 1, pp 43-52, Norwich, United Kingdom, September 9-11 2003.
  
  
• B.-J. Theobald, J. A. Bangham, I. Matthews, G. Cawley, Evaluation of a talking head based on appearance models, in Proceedings of the International Conference on Audio-Visual Speech Processing (AVSP-2003), St Jorioz, France, September 4-7 2003.
  
  
• S. Cox and G. Cawley, The use of confidence measures in vector based call-routing, in Proceedings of Eurospeech-2003, Geneva, Switzerland, pp. 633-636, September 1-4 2003.
  
  
• D. Bescoby, G. C. Cawley and N. Chroston, Interpretation of geophysical surveys of archaeological sites using artificial neural networks, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2003), vol. 2, pp 1132-1137, Portland, Oregon, USA, July 20-24 2003. (pdf)
  
  
• A. J. Bagnall and G. C. Cawley, Learning classifier systems for data mining : A comparison of XCS with other classifiers for the Forest Cover dataset, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2003), vol. 3, pp 1802-1807, Portland, Oregon, USA, July 20-24 2003. (pdf)
  
  
• Y. Lan, G. C. Cawley and R. W. Harvey, Train-spotting: Building classifiers for microarrays, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2003), vol. 4, pp 2934-2939, Portland, Oregon, USA, July 20-24 2003.
  
  
• R. J. Foxall, G. C. Cawley and M. W. Peck, Modelling the growth domain of Clostridium botulinum via kernel survival analysis, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2003), vol. 4, pp 2958-2963, Portland, Oregon, USA, July 20-24 2003. (pdf)
  
  
• G. C. Cawley, S. R. Dorling, P. D. Jones and C. Goodess, Statistical downscaling with artificial neural networks, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003), pages 167-172, Bruges, Belgium, April 23-25 2003. (pdf)
  
  
• G. C. Cawley, N. L. C. Talbot, R. J. Foxall, S. R. Dorling and D. P. Mandic, Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003), pages 209-214, Bruges, Belgium, April 23-25 2003. (pdf)
  
  
• G. C. Cawley and N. L. C. Talbot, Efficient cross-validation of kernel Fisher discriminant classifiers, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003), pages 241-246, Bruges, Belgium, April 23-25 2003. (pdf)
  
  
• B.-J. Theobald, G. C. Cawley, I. A. Matthews and J. A. Bangham, Near-videorealistic synthetic visual speech using non-rigid appearance models, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2003), vol. V, pp 804-807, Hong Kong, April 6-10 2003.
  
  
• L. Partanen, J. Kukkonen, A. Karpinnen, J. Ruuskanen, T. Patama, M. Kolehmainen, S. Dorling, R. Foxall and G. Cawley, Inter-comparison of neural network, statistical and deterministic models for predicting the concentrations of NO2 and PM10 in urban air, in Proceedings of the Fourth International Conference on Urban Air Quality - Measurement, Modelling and Management, R. S. Sokhi and J. Brechler (Eds), Charles University, Prague, Czech Republic, pp 274-277, March 25-27 2003.

2002

• G. C. Cawley and N. L. C. Talbot, Reduced rank kernel ridge regresion, Neural Processing Letters, vol. 16, no. 3, pp 293-302, December 2002. (pdf, doi)
  
  
• G. C. Cawley and N. L. C. Talbot, Improved sparse least-squares support vector machines, Neurocomputing, vol. 48, no. 1-4, pp 1025-1031, October 2002. (pdf, doi)
  
  
• J. Kukkonen, A. Karppinen, L. Wallenius, J. Ruuskanen, T. Patama, M. Kolehmainen, S. Dorling, R. Foxall, G. Cawley, D. Mandic, T. Chatterton, M. Zickus and A. Greig, Evaluation of neural network, statistical and deterministic models against the measured concentrations of NO2, PM10 and PM2.5 in an urban area, in Proceedings of the Eighth International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, E. Batchvarova and D. Syrakov (Eds), pp. 63-67, Sofia, Bulgaria, October 14-17 2002.
  
  
• G. C. Cawley and N. L. C. Talbot, A Greedy Training Algorithm for Sparse Least-Squares Support Vector Machines, in Proceedings of the International Conference on Artificial Neural Networks (ICANN-2002), Springer Lecture Notes in Computer Science (LNCS), vol. 2415, pp. 681-686, Madrid, Spain, August 27-30 2002. (pdf)
  
  
• R. J. Foxall, G. C. Cawley, S. R. Dorling and D. P. Mandic, Error Functions for Prediction of Episodes of Poor Air Quality, in Proceedings of the International Conference on Artificial Neural Networks (ICANN-2002), Springer Lecture Notes in Computer Science (LNCS), vol. 2415, pp. 1031-1036, Madrid, Spain, August 27-30 2002. (pdf)
  
  
• A. Bosson, G. C. Cawley, Y. Chan and R. Harvey, Non-retrieval : blocking pornographic images, in Proceedings of the International Conference on Image and Video Retrieval (CIVR-2002), M. S. Lew, N. Sebe and J. P. Eakins (Eds.), Springer Lecture Notes in Computer Science (LNCS), vol. 2383, pp. 50-60, London, United Kingdom, July 18-19 2002. (pdf)
  
  
• B. Theobald, J. A. Bangham, I. Matthews and G. C. Cawley, Towards video realistic synthetic visual speech, In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2002), pp. 3892-3895, Orlando, Florida, USA, May 13-17 2002.
  
  
• G. C. Cawley and N. L. C. Talbot, Efficient formation of a basis in a kernel induced feature space, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002), pp. 1-6, Bruges, Belgium, April 24-26 2002. (pdf)
  
  
• R. J. Foxall, G. C. Cawley, N. L. C. Talbot, S. R. Dorling and D. P. Mandic, Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002), pp. 19-24, Bruges, Belgium, April 24-26 2002. (pdf)
  
  
• K. Saadi, G. C. Cawley and N. L. C. Talbot, Fast exact leave-one-out cross-validation of least-squares support vector machines, In Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002), pp. 149-154, Bruges, Belgium, April 24-26 2002. (pdf)

2001

• O. Yli-Harja, P. Koivisto, J. A. Bangham, G. Cawley, R. Harvey and I. Shmulevich, Simplified implementation of the recursive median sieve, Signal Processing, vol. 81, no. 7, pp 1565-1570, July 2001. (doi)
  
  
• B. Theobald, G. Cawley, S. Kruse and J. A. Bangham, Towards a low bandwidth talking face using appearance models, In Proceedings of the British Machine Vision Conference (BMVC-2001), pp. 583-592, T. F. Cootes and C. J. Taylor (Eds), BMVA Press, Manchester, United Kingdom, September 10-13 2001.
  
  
• B. J. Theobald, J. A. Bangham, I. Matthews and G. Cawley, Visual speech synthesis using statistical models of shape and appearance, In Proceedings of the International Conference on Auditory-Visual Speech Processing (AVSP-2001), pp. 78-83, D. Massaro (Ed), Aalborg, Denmark, September 7-9 2001.
  
  
• G. C. Cawley and N. L. C. Talbot, Manipulation of prior probabilities in support vector classification, In Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2001), pp. 2433-2438, Washington, D.C., U.S.A., July 15-19 2001. (pdf)
  
  
• B. J. Theobald, S. Kruse, J. A. Bangham and G. C. Cawley, Towards videorealistic synthetic visual speech, In Proceedings of the Workshop on the Management of Uncertainty in Geometric Computations, J. Winkler and M. Niranjan (Eds), Kluwer Academic Publishers, Sheffield, United Kingdom, pp. 175-184, July 5-6 2001.
  
  
• R. Foxall, I. Krcmar, G. Cawley, S. Dorling and D. P. Mandic, Nonlinear modelling of air pollution time series, In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2001), vol. VI, pp. 3505-3508, Salt Lake City, Utah, U.S.A.", May 7-11 2001.
  
  
• G. C. Cawley, Efficient Sequential Minimal Optimisation of Support Vector Classifiers, In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 430-433, Prague, Czech Republic, April 2001. (Paper : pdf)
  
  
• G. C. Cawley, Model Selection for Support Vector Machines via Adaptive Step-Size Tabu Search", In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 434-437, Prague, Czech Republic, April 2001. (Paper : pdf)
  
  
• Foxall, R. J., Krcmar, I., Cawley, G. C., Dorling, S. R. and Mandic, D. P., On Nonlinear Processing of Air Pollution Data, In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 477-480, Prague, Czech Republic, April 2001. (pdf)
  
  
• Cawley, G. C., Dorling, S. R., Foxall, R. J., and Mandic, D. P., Estimating the Costs Associated with Worthwhile Predictions of Poor Air Quality, In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 485-488, Prague, Czech Republic, April 2001. (pdf)

2000

• G. C. Cawley, On a fast compact approximation of the exponental function, Neural Computation, vol. 12, no. 9, pp 2009-20012, September 2000. (pdf, PubMed)
  
  
• A. J. Grieg, G. Cawley, S. Dorling, K. Eben, A. J. Fiala, A. Karpinnen, J. Keder, M. Kolehmainen, J. Kukkonen, B. Libero, J. Macoun, M. Niranjan, A. Nucifora, G. Nunnari, M. Palus, E. Pelikan, J. Ruuskanan and U. Schlink, Air pollution episodes : modelling tools for improved smog management (APPETISE), In Proceedings of the Eighth International Conference on Air Pollution, J. W. S. Longhurst, C. A. Brebbia and H. Power, pp. 89-98, New Hall, Cambridge University, United Kingdom, July 24-26 2000.

1999

• B. Theobald, S. J. Cox, G. C. Cawley and B. Milner, A fast method of channel equalisation for speech signals and its implementation on a DSP, Electronics Letters, vol. 35, no. 16, pp 1309-1311, August 1999.

1998

• G. C. Cawley and M. D. Edgington, Generalisation in neural speech synthesis, In Proceedings of the Institute of Acoustics Autumn Conference (Speech and Hearing 98), Windemere, U.K., November 1998. (pdf)
  
  
• J. A. Bangham, J. R. Hidalgo, R. W. Harvey, and G. C. Cawley, The segmentation of images using scale-space trees, In Proceedings of the British Machine Vision Conference (BMVC-98), Southampton, U.K., September 1998. (pdf)
  
  
• G. C. Cawley, M. W. Peck, and P. S. Fernández, A neural model of time to toxin production by non-proteolytic Clostridium botulinum, In Proceedings of the I.E.E.E. International Joint Conference on Neural Networks (IJCNN-98), part of the 1998 I.E.E.E World Congress on Computational intelligence, Anchorage, Alaska, U.S.A., May 1998. (pdf)
  

1997

• L. M. Teixeira de Jesus and G. C. Cawley, Speech coding using parametric curves, In Proceedings of the 5th European Conference on Speech Communications and Technology (EuroSpeech-97), volume 2, pages 597-600, Rhodes, Greece, September 1997. (pdf)
  
  
• N. L. C. Talbot and G. C. Cawley, A fast index assignment method for robust vector quantisation of image data, In Proceedings of the I.E.E.E. International Conference on Image Processing (ICIP-97), volume 3, pages 674-677, Santa Barbara, California, U.S.A., October 1997. (pdf)

1996

• G. C. Cawley and N. L. C. Talbot, A fast index assignment algorithm for vector quantization over nosiy transmission channels, Electronics Letters, vol. 32, no. 15, pp 1343-1344, July 1996. (pdf)
  
  
• G. C. Cawley and S. R. Dorling, Reproducing a subjective classification scheme for atmospheric circulation patterns over the united kingdom using a neural network, In Proceedings of the International Conference on Artificial Neural Networks (ICANN-96), pages 281-286, July 1996. (pdf)
  
  
• G. C. Cawley, An improved vector quantisation algorithm for speech transmission over noisy channels, In Proceedings International Conference Spoken Language Processing (ICSLP-96), volume 1, pages 299-301, October 1996. (pdf)
  
  
• N. L. C. Talbot and G. C. Cawley, A quadratic index assignment algorithm for vector quantisation over noisy transmission channels, In Proceedings of the Institute of Acoustics Autumn Conference (Speech and Hearing 96), volume 18, pages 195-199, November 1996. (pdf)
  
  
• G. C. Cawley, The Application of Neural Networks to Phonetic Modelling, PhD thesis, Department of Electronic Systems Engineering, University of Essex, Colchester, Essex, U.K., March 1996.

Ancient History

• G. C. Cawley and P. D. Noakes, The use of vector quantization in neural speech synthesis, In Proc. IJCNN-94, volume 3, pages 2227-2230, Nagoya, Japan, 1994. (pdf)
  
  
• S. Lucas, Z. Zhao, G. Cawley and P. Noakes, Pattern recognition with the decomposed multi-layer perceptron, Electronics Letters, vol. 29, no. 5, pp 442-443, March 1993. (pdf)
  
  
• G. C. Cawley and P. D. Noakes, L.S.P. speech synthesis using a neural network, In Proceedings of the I.E.E. International Conference on Artificial Neural Networks, pages 291-294, Brighton, UK, 1993. (pdf)
  
  
• G. C. Cawley, M. I. Heywood, and P. D. Noakes, Weight zero enhancement in speech synthesis using neural networks, In Proceedings the International Conference on Artificial Neural Networks (ICANN-93), page 272, Amsterdam, September 1993. (pdf)
  
  
• G. C. Cawley and P. D. Noakes, Allophone synthesis using a neural network, In Proceedings of the World Conference on Neural Networks, volume 2, pages 122-125, Portland, Oregon, USA, July 1993. (pdf)
  
  
• S. Lucas, Z. Zhao, G. Cawley, and P. Noakes, On decomposing MLPs, In Proceedings of the IEEE International Conference on Neural Networks (ICNN-93), pages 1414-1418, San Fransisco, California, U.S.A., March 1993.
  
  
• G. C. Cawley and P. D. Noakes, Diphone synthesis using a neural network, In Proceedings of the International Conference on Artificial Neural Networks (ICANN-92), volume 1, pages 795-798, Brighton, UK, September 1992. (pdf)
  
  
• G. C. Cawley and A. D. P. Green, The application of neural networks to cognitive phonetic modelling, In Proceedings of the I.E.E. International Conference on Artificial Neural Networks, pages 280-284, Bournemouth, U.K., November 1991. (pdf)