Publication

Uncertainty Feature Optimization: an implicit paradigm for problems with noisy data

Michel Bierlaire, Matteo Salani
2011
Journal Articles

Abstract

Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise.

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Uncertainty Feature Optimization: an implicit paradigm for problems with noisy data

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