@article {SANCHEZ20141583, title = {Comments on {\textquotedblleft}Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization{\textquotedblright} by Eyke H{\"u}llermeier}, journal = {International Journal of Approximate Reasoning}, volume = {55}, number = {7}, year = {2014}, note = {Special issue: Harnessing the information contained in low-quality data sources}, pages = {1583 - 1587}, abstract = {The paper by Eyke H{\"u}llermeier introduces a new set of techniques for learning models from imprecise data. The removal of the uncertainty in the training instances through the input{\textendash}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.}, keywords = {classification, fuzzy data, Imprecise data, Loss functions, machine learning, Regression}, issn = {0888-613X}, doi = {https://doi.org/10.1016/j.ijar.2014.04.008}, url = {http://www.sciencedirect.com/science/article/pii/S0888613X14000607}, author = {Luciano S{\'a}nchez} }