WP7: Learning complete super models

This workpackage will develop machine learning methods for learning complete supermodels. We expect that supermodels will be built in three phases. The machine learning methods will first generate diverse models, then select a set of complementary models, and finally learn the interconnections between the constituent models of an ensemble. These three phases naturally lead to the three tasks that constitute this WP.
JSI, which has ample expertise in machine learning, will lead this WP and the constituent tasks. MASA will also contribute significantly, in close collaboration with JSI, providing insights from non-linear dynamics.

WP7 is chaired by Saso Dzeroski

work package layout of SUMO