Error-driven active learning in growing radial basis function networks for early robot learning

Q. Meng and M.H. Lee, 'Error-driven active learning in growing radial basis function networks for early robot learning', 2006 IEEE International Conference on Robotics and Automation (IEEE ICRA 2006), 2984-90, Orlando, Florida, USA.

An association mapping approach to identify flowering time genes in natural populations of Lolium perenne (L.)

Sk?t, L., Humphreys, M. O., Armstead, I. P., Heywood, S., Sk?t, K. P., Sanderson, R., Thomas, I. D., Chorlton, K. H., & Sackville Hamilton, N. R. (2005). An association mapping approach to identify flowering time genes in natural populations of Lolium perenne (L.). Molecular Breeding, 15(3), 233-245. Sponsorship: BBSRC RAE2008

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

Application of metabolomics to plant genotype discrimination using statistics and machine learning

Janet Taylor, Ross D King, Thomas Altmann and Oliver Fiehn (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. 1st European Conference on Computational Biology (ECCB). (published as a journal supplement in Bioinformatics 18: S241-S248).

How well do we understand the clusters found in microarray data?

Clare, A. and King R.D. (2002) How well do we understand the clusters found in microarray data? In In Silico Biol. 2, 0046

GC-MS Peak Labeling Under ArMet

Jenkins, H., Beckmann, M., Draper, J., & Hardy, N. (2007). GC-MS Peak Labeling Under ArMet. Nikolau, B. J., & Wurtele, E. S. (Eds.), In: Concepts in Plant Metabolomics. (pp. 19-28). Dordrecht: Springer. Sponsorship: The authors gratefully acknowledge the United Kingdom Food Standards Agency (under the G02006 project) for support of their work in metabolomics.