Abstract. Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the re...
Axel J. Soto, Marc Strickert, Gustavo E. Vazquez, ...
Abstract. The Self-Organising Map (SOM) is a well-known neuralnetwork model that has successfully been used as a data analysis tool in many different domains. The SOM provides a to...
Background: Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large d...
Geraint Barton, J. C. Abbott, Norie Chiba, D. W. H...
Abstract—Cyber Physical Systems are distributed systemsof-systems that integrate sensing, processing, networking and actuation. Aggregating physical data over space and in time e...
Cristian Ferent, Varun Subramanian, Michael Gilber...
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...