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

International Journal of Approximate Reasoning

Volumn 55, Issue 7, 2014, Pages 1588-1590

  Destercke, S ^a  

^a CNRS   (France)

Author keywords

Inference; Learning; Uncertain data; Uncertain models

Indexed keywords

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

INFERENCE; LEARNING; LEARNING PROCEDURES; LOSS MINIMIZATION; MACHINE LEARNING APPROACHES; UNCERTAIN DATAS; UNCERTAIN MODELS; UNCERTAINTY REPRESENTATION;

LEARNING SYSTEMS;

EID: 84905113106     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2014.04.014     Document Type: Note

References (6)

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2. D. Dubois, and H. Prade When upper probabilities are possibility measures Fuzzy Sets Syst. 49 1992 65 74
3. E. Hüllermeier Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization Int. J. Approx. Reason. 55 7 2014 1519 1534 10.1016/j.ijar.2013.09.003 (in this issue)
4. E. Miranda, and S. Destercke Extreme points of the credal sets generated by elementary comparative probabilities ECSQARU 2013 424 435
5. M. Troffaes Decision making under uncertainty using imprecise probabilities Int. J. Approx. Reason. 45 2007 17 29 (Pubitemid 46617871)
6. P. Walley Statistical Reasoning with Imprecise Probabilities 1991 Chapman and Hall New York