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• W2131771566 abstract "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.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
• W2131771566 created "2016-06-24" @default.
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• W2131771566 date "1995-05-01" @default.
• W2131771566 modified "2023-10-18" @default.
• W2131771566 title "Nonparametric estimation via empirical risk minimization" @default.
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• W2131771566 doi "https://doi.org/10.1109/18.382014" @default.
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