Scalable machine learning systems will underpin the complex data analytics afforded by BigData. The goal of the FlexGP project
is scaled machine learning which exploits cloud-scale parallelism and resource elasticity. FlexGP launches diverse learning engines onto the cloud which are different along training data partitions and explanatory dimensions as well as model expression and objective function choices. Among other related questions, the Evolutionary Design and Optimization Group is working to learn how much data is enough in the context of BigData's high dimensionality and volume and how the diverse outcomes of factored learners can be efficiently fused.
FlexGP project: http://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/index.php?n=Site.FlexGP