Possibilistic classifiers for numerical data
نویسندگان
چکیده
منابع مشابه
Possibilistic classifiers for numerical data
Naive Bayesian Classifiers, which rely on independence hypotheses, together with a normality assumption to estimate densities for numerical data, are known for their simplicity and their effectiveness. However, estimating densities, even under the normality assumption, may be problematic in case of poor data. In such a situation, possibility distributions may provide a more faithful representat...
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ژورنال
عنوان ژورنال: Soft Computing
سال: 2012
ISSN: 1432-7643,1433-7479
DOI: 10.1007/s00500-012-0947-9