This paper presents an approach to improving the classification performance of a multiple category theory by correcting intermediate rules which are shared among the categories. Using this technique, the performance of a theory in one category can be improved through training in an entirely different category. Examples of the technique are presented and experimental results are given.

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In Proceedings of the Eighth International Workshop on Machine Learning, pp. 534-538, Evanston, IL, June 1991.

@InProceedings{ourston:ml91, title={Improving Shared Rules in Multiple Category Domain Theories}, author={Dirk Ourston and Raymond J. Mooney}, booktitle={Proceedings of the Eighth International Workshop on Machine Learning}, month={June}, address={Evanston, IL}, key={EITHER}, pages={534-538}, url="http://www.cs.utexas.edu/users/ai-lab?ourston:ml91", year={1991} }