Inductive Learning of Characteristic Concept Description from Small Sets of Classified Examples
نویسنده
چکیده
This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classiied examples, the approach presented in the paper is aimed at being applicable when only a few examples are classiied as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassiied objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented.
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