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

Artículo de revista 2014

Luciano Sánchez

International Journal of Approximate ReasoningVolumen 55Páginas 1583-1587

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Sánchez, L. (2014). Comments on Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization by Eyke Hüllermeier. International Journal of Approximate Reasoning, 55, 1583-1587. https://doi.org/10.1016/j.ijar.2014.04.008
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Resumen

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.

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Detalles

DOI
10.1016/j.ijar.2014.04.008
ISSN
0888-613X
Notas

Special issue: Harnessing the information contained in low-quality data sources