Items where Division is "Engineering & Applied Sciences > Computer science research group" and Year is 2006
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Adams, Ann, Buckingham, Christopher D., Arber, Sara, McKinlay, John B., Marceau, Lisa and Link, Carol (2006). The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK. Ageing and Society, 26 (2), pp. 303-321.
Jones, Anthony and Cornford, Dan (2006). A flexible, extensible object oriented real-time near photorealistic visualization system:the system framework design. IN: Progress in Spatial Data Handling: 12th International Symposium on Spatial Data Handling. Springer.
Kondratova, Irina, Lumsden, Joanna and Langton, Nathan (2006). Multimodal field data entry:performance and usability issues. IN: The proceedings of the joint international conference on computing and decision making in civil and building engineering 2006. Rotterdam (NL): UNSPECIFIED.
Maniyar, Dharmesh M. and Nabney, Ian T (2006). Visual data mining: integrating machine learning with information visualization. IN: Workshop on Multimedia Data Mining “Merging Multimedia and Data Mining Research”. Zhang, Zhongfei; Masseglia, Florent; Jain, Ramesh and Del Bimbo, Alberto (eds) ACM.
Maniyar, Dharmesh M. and Nabney, Ian T. (2006). Data visualization with simultaneous feature selection. IN: Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06. UNSPECIFIED.
Matthews, Robert (2006). Profiling of CSF:Reliability of diagnosis. PLoS Medicine, 3 (10),
Reeves, Trevor, Cornford, Dan, Konecny, Michal and Ellis, Jeremy (2006). Modeling geometric rules in object based models:an XML / GML approach. IN: Progress in Spatial Data Handling: 12th International Symposium on Spatial Data Handling. Springer.
Wang, X. X., Chen, S., Lowe, D. and Harris, C. J. (2006). Sparse support vector regression based on orthogonal forward selection for the generalised kernel model. Neurocomputing, 70 (1-3), pp. 462-474.
Woodcock, D. and Nabney, Ian T. (2006). A new entropy measure based on the Renyi entropy rate using Gaussian kernels. Technical Report. Aston University, Birmingham.