Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection,' Proceedings of the Fifth International Conference on Rough Sets and Current Trends in Computing (RSCTC 2006), LNAI 4259, pp. 147-156, 2006.

A novel breast tissue density classification framework

Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E., Zwiggelaar, R. (2008). A novel breast tissue density classification framework. IEEE Transactions on Information Technology in BioMedicine, 12 (1), 55-65

Inspection of teats by colour image analysis for automatic milking systems

C.R. Bull, N.J.B. McFarlane, R. Zwiggelaar, C.J. Allen and T.T. Mottram, 'Inspection of teats by colour image analysis for automatic milking systems', Computers and Electronics in Agriculture 15 (1), 15-26 (1996)

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35

Research into automatic error recovery

Lee, M., Hardy, N., & Barnes, D. P. (1984). Research into automatic error recovery. 65-69. Paper presented at 4th International Conference on Robot Vision and Sensory Controls, London, London, United Kingdom.

Automatic Error Recovery in Behaviour-Based Assistive Robots with Learning from Experience

Meng Q. and Lee M.H., Automatic Error Recovery in Behaviour-Based Assistive Robots with Learning from Experience, in Proc. INES 2001, 5th IEEE Int. Conf. on Intelligent Engineering Systems, Helsinki, Finland, Sept 2001, pp291-296.

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

Aiding classification of gene expression data with feature selection: a comparative study

C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: a comparative study. Computational Intelligence Research, 1(1):68-76.

Evolutionary approaches to fuzzy modelling for classification

M. Galea, Q. Shen and J. Levine. Evolutionary approaches to fuzzy modelling. Knowledge Engineering Review, 19(1):27-59, 2004.

Subsethood-based fuzzy modelling and classification

K. Rasmani and Q. Shen. Subsethood-based fuzzy modelling and classification. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 181-188.

Automatic 3d free form shape matching using the graduated assignment algorithm.

Liu, Yonghuai. Automatic 3d free form shape matching using the graduated assignment algorithm. Pattern Recognition, vol. 38, no. 10, pp. 1615-1631, 2005.

Biologically inspired automatic construction of cross-modal mapping in robotic eye/hand systems

Q. Meng and M.H. Lee, 'Biologically inspired automatic construction of cross-modal mapping in robotic eye/hand systems', IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006,) ,4742-49, Beijing, 2006.

Linear structures in mammographic images: detection and classification

R. Zwiggelaar, S.M. Astley, C.J. Taylor and C.R.M. Boggis, 'Linear structures in mammographic images: detection and classification', IEEE Transaction on Medical Imaging 23 (9), 1077-1086 (2004)

Automatic point correspondence and registration based on linear structures

R. Marti, R. Zwiggelaar, C.M.E. Rubin, 'Automatic point correspondence and registration based on linear structures', International Journal of Pattern Recognition and Artificial Intelligence 16 (3), 331-340 (2002)

Report for the British Board of Film Classification on viewers of the Lord of the Rings, aged under 16

This is a report on what can be learnt from our world dataset about viewers of The Lord of the Rings who were aged under 16. In this report, I draw both on the world set, and on the UK subset, sometimes drawing comparisons between them. The reason for using both is that, obviously, the world set is so much larger (comprising 24,739 in toto, with 2475 under 16), but the UK set (comprising 3115 in toto, and 306 under 16s) allows us to explore both some of the specificities of responses here, the qualitative meaning of some responses (given we worked in 14 languages, many are inaccessible to us for analysis), and of course their relations to the quantitative patterns that emerge.