Learning from Data as an Optimization and Inverse Problem
Learning form data is investigated as minimization of empirical error functional in spaces of continuous functions and spaces defined by kernels. Using methods from theory of inverse problems, an alternative proof of Representer Theorem is given. Regularized and non regularized minimization of empirical error is compared.
learning from data inverse problems minimization of empirical error reproducing kernel hilbert spaces