Principles of Risk Minimization for Learning Theory

Learning is posed as a problem of function estimation, for which two princi(cid:173) ples of solution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different state(cid:173) ments of the function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.