Principles of Risk Minimization for Learning Theory

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

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V. Vapnik


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.