Principles of Risk Minimization for Learning Theory
Vladimir N. Vapnik · Neural Information Processing Systems · 1991
Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk minimization and structural risk minimization. These two principles are applied to two different statements of the function estimation problem: global and local. Systematic improvements in prediction power are illustrated in application to zip-code recognition.