Proceedings of the Eleventh International Workshop on Machine Learning pp . 173 - 180 ,

نویسنده

  • Raymond J. Mooney
چکیده

This paper compares two methods for re ning uncertain knowledge bases using propositional certainty-factor rules. The rst method, implemented in the Rapture system, employs neural-network training to rene the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential new rules with very low certainty and allows neural-network training to lter and adjust these rules. Experimental results indicate that the former method results in signi cantly faster training and produces much simpler re ned rule bases with slightly greater accuracy.

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تاریخ انتشار 1994