Abstract
Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to (1) be defined with respect to the same set of relevant attributes, (2) be disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We describe a supervised, incremental, instance-based learning algorithm (Bloom) that removes these assumptions. Bloom learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept descriptions.
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