C.-C. Chan, #Incremental learning of production rules from examples under uncertainty: A rough set approach", International Journal of Software Engineering and Knowledge Engineering 1 #1991# 439#461. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
RIAC: A Rule Induction Algorithm Based on Approximate.. - Hamilton, Shan, Cercone (1996) (3 citations) (Correct)
....knowledge. Knowledge is imprecise if it contains any imprecise concept. A concept is precise if it can be expressed (defined) in terms of the assumed classification patterns; otherwise the concept is imprecise (Pawlak 1991) Applications of rough sets to machine learning area are given in (Chan 1991; Gezymala Busse 1988; Slowinski 1992; Pawlak 1991) Learnability of concepts is another important issue in inductive learning. If the learning task is to generate a description of a target concept based on a set of condition attributes, the whether or not it can be done depends on the granularity ....
Chan, C.C. 1991. "Incremental Learning of Production Rules from Examples under Uncertainty: A Rough Set Approach," International Journal of Software Engineering and Knowledge Engineering, 1(4):439-461.
Uncertainty Management - Berztiss (Correct)
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C.-C. Chan, #Incremental learning of production rules from examples under uncertainty: A rough set approach", International Journal of Software Engineering and Knowledge Engineering 1 #1991# 439#461.
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