Abstract
This paper presents results comparing three simple inductive learning systems using different representations for concepts, namely: CNF formulae, DNF formulae, and decision trees. The CNF learner performs surprisingly well. Results on five natural data sets indicates that it frequently trains faster and produces more accurate and simpler concepts.
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