Discretization Error Analysis for Tikhonov Regularization in Learning Theory
We study the connections between learning from examples and inverse problems. We show that learning from examples can be seen as the discretization of a stochastic inverse problem defined by a Carleman operator. In particular we develop a discretization strategy for this class of inverse problems and we give a convergence analysis. Our approach can be applied to other classes of problems such as integral equations.