Title :
Nonparametric estimation via empirical risk minimization
Author :
Lugosi, Gábor ; Zeger, Kenneth
Author_Institution :
Dept. of Math. & Comput. Sci., Tech. Univ. Budapest, Hungary
fDate :
5/1/1995 12:00:00 AM
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
Keywords :
curve fitting; estimation theory; learning (artificial intelligence); minimisation; neural nets; nonparametric statistics; pattern recognition; conditional median estimation; curve fitting; distribution-free almost sure consistency; empirical error minimisation; empirical risk minimization; generalized linear estimators; learning concepts; neural network estimates; nonparametric estimation; nonparametric estimators; pattern recognition; regression estimation; universal consistency; Computer errors; Computer science; Convergence; Curve fitting; Mathematics; Neural networks; Pattern recognition; Random variables; Risk management; Training data;
Journal_Title :
Information Theory, IEEE Transactions on
