This paper describes INC2, an incremental category formation system which implements the concepts of family resemblance, contrast-model-based similarity, and context-sensitive, distributed probabilistic representation. The system is evaluated in terms of both the structure of categories/hierarchies it generates and its categorization (prediction) accuracy in both noise-free and noisy domains. Performance is shown to be comparable to both humans and existing leaming-from-example systems, even though the system is not provided with any category membership information during the category formation stage.
