Comments on 'Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization' by Eyke Huellermeier

摘要

The paper by Eyke Hiillermeier introduces a new set of techniques for learning models from imprecise data. The removal of the uncertainty in the training instances through the input-output relationship described by the model is also considered. This discussion addresses three points of the paper: extension principle-based models, precedence operators between fuzzy losses and possible connections between data disambiguation and data imputation.