Recent results in both machine learning and cognitive psychology demonstrate that effective category learning involves an integration of theory and data. First, theories can bias induction, affecting what category definitions are extracted from a set of examples. Second, conflicting data can cause theories to be revised. Third, theories can alter the representation of data through feature formation. This chapter reviews two machine learning systems that attempt to integrate theory and data in one or more of these ways. IOU uses a domain theory to acquire part of a concept definition and to focus induction on the unexplained aspects of the data. EITHER uses data to revise an imperfect theory and uses theory to add abstract features to the data. Recent psychological experiments reveal that these machine learning systems exhibit several important aspects of human category learning. Specifically, IOU has been used to successfully model some recent experimental results on the effect of functional knowledge on category learning.

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In Categorization by Humans and Machines, G. V. Nakamura and D. L. Medin and R. Taraban (Eds.), pp. 189-218 1993.

@InCollection{mooney:chm93, title={Integrating Theory and Data in Category Learning}, author={Raymond J. Mooney}, booktitle={Categorization by Humans and Machines}, editor={G. V. Nakamura and D. L. Medin and R. Taraban}, pages={189-218}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:chm93", year={1993} }