Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization

International Journal of Approximate Reasoning

Volumn 55, Issue 7, 2014, Pages 1519-1534

  Hüllermeier, Eyke ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

Data disambiguation; Extension principle; Fuzzy sets; Imprecise data; Loss function; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUZZY SETS; LEARNING SYSTEMS;

BINARY CLASSIFICATION; DATA DISAMBIGUATION; EMPIRICAL RISK MINIMIZATION; EXTENSION PRINCIPLES; IMPRECISE DATA; LOGISTIC REGRESSIONS; LOSS FUNCTIONS; MACHINE LEARNING METHODS;

LEARNING ALGORITHMS;

EID: 84905111276     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2013.09.003     Document Type: Conference Paper

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