Classification and regularization in learning theory
In this dissertation, we research classification algorithms created by regularization schemes. The design of these algorithms and their error analysis are completely explained. These algorithms rely on convex risk minimization with Tikhonov regularization. They require an admissible convex loss function, a hypothesis