Support Vector Machines are learning machines that can perform binary classification (pattern recognition) and real valued function approximation (regression estimation) tasks. Support Vector Machines non-linearly map their n-dimensional input space into a high dimensional feature space. In this high dimesional feature space a linear classifier is constructed. For further information see our publications list.
If you are interested in developing commercial applications based on the Support Vector Machine, Transduction and related techniques, please contact the Director of the Computer Learning Research Centre, Professor A. Gammerman (email@example.com).
SVM Web site implementation -25. June 2013