Are We Better Oo without Counter-examples?

Concept-learning is commonly implemented using discrimination-based techniques which rely on both examples and counterexamples of the concept. Recently, however, a recognition-based approach that learns a concept in the absence of counterexamples was shown to be more accurate than its discrimination counterpart on two real-world domains and as accurate on the third. The purpose of this paper is to nd out whether this recognition-based approach is generally more accurate than its discrimination counterpart or whether the results it obtained previously are purely coinci-dental. 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.