Nonparametric estimation via empirical risk minimization
Lugosi, G.; Zeger, K.
Information Theory, IEEE Transactions on
Volume 41, Issue 3, May 1995 Page(s):677 - 687
Digital Object Identifier   10.1109/18.382014
Summary:A general notion of universal consistency of nonparametric estimators is introduced that applies to regression estimation, conditional median estimation, curve fitting, pattern recognition, and learning concepts. General methods for proving consistency of estimators based on minimizing the empirical error are shown. In particular, distribution-free almost sure consistency of neural network estimates and generalized linear estimators is established

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