@article{532,
  keywords = {classification, fuzzy data, Imprecise data, Loss functions, machine learning, Regression},
  author = {Luciano Sánchez},
  title = {Comments on   Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization   by Eyke Hüllermeier},
  abstract = {The paper by Eyke Hüllermeier 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.},
  year = {2014},
  journal = {International Journal of Approximate Reasoning},
  volume = {55},
  number = {7},
  pages = {1583-1587},
  issn = {0888-613X},
  url = {http://www.sciencedirect.com/science/article/pii/S0888613X14000607},
  doi = {10.1016/j.ijar.2014.04.008},
  note = {Special issue: Harnessing the information contained in low-quality data sources},
}