Learning cross-cutting systems of categories. Shafto, P., Kemp, C., Mansinghka, V. K., Gordon, M., & Tenenbaum, J. B. In NIPS 2006: Proceedings of the 28th Annual Conference of the Cognitive Science Society, pages 2146–2151, 2006.
Learning cross-cutting systems of categories [pdf]Paper  Learning cross-cutting systems of categories [link]Link  abstract   bibtex   
Most natural domains can be represented in multiple ways: animals may be thought of in terms of their taxonomic groupings or their ecological niches and foods may be thought of in terms of their nutritional content or social role. We present a computational framework that discovers multiple systems of categories given information about a domain of objects and their properties. Each system of object categories accounts for a distinct and coherent subset of the features. A first experiment shows that our CrossCat model predicts human learning in an artificial category learning task. A second experiment shows that the model discovers important structure in two real-world domains. Traditional models of categorization usually search for a single system of categories: we suggest that these models do not predict human performance in our task, and miss important structure in our real world examples.
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