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

Raymond J. Mooney Faculty mooney [at] cs utexas edu
Dirk Ourston Ph.D. Alumni ourston [at] arlut utexas edu