  We deal with the task of supervised learning if the data is of functional
type. The crucial point is the choice of the appropriate fitting method
(learner). Boosting is a stepwise technique that combines learners in such a
way that the composite learner outperforms the single learner. This can be done
by either reweighting the examples or with the help of a gradient descent
technique. In this paper, we explain how to extend Boosting methods to problems
that involve functional data.
