Multi Facet Learning in Hilbert Spaces

We extend the kernel based learning framework to learning from linear functionals, such as partial derivatives. The learning problem is formulated as a generalized regularized risk minimization problem, possibly involving several dieren t functionals. We show how to reduce this to conventional kernel based learning methods and explore a specic application in Computational Condensed Matter Physics.