Abstract

Concept-learning is commonly implemented using discrimination-based techniques which rely on both examples and counter-examples of the concept. Recently, however, a recognition-based approach that learns a concept in the absence of counter-examples was shown to be more accurate than its discrimination counterpart on two realworld domains and as accurate on the third. The purpose of this paper is to find out whether this recognition-based approach is generally more accurate than its discrimination counterpart or whether the results it obtained previously are purely coincidental. The analysis conducted in this paper concludes that the results obtained on the real-world domains were not coincidental, and this suggests that recognition-based approaches are promising techniques worth studying in greater depth. 1 Introduction Concept-learning can be approached in one of two ways. The first way consists of considering both examples and counter-examples of the concept at hand and learning ho...

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