'Columbia University Libraries/Information Services'
Doi
Abstract
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 different functionals. We show how to reduce this to conventional kernel based learning methods and explore a specific application in Computational Condensed Matter Physics
