Improving Accuracy of Incorrect Domain Theories

نویسنده

  • Lars Asker
چکیده

An approach to improve accuracy of incorrect domain theories is presented that learns concept descriptions from positive and negative examples of the concept. The method uses the available domain theory, that might be both overly general and overly speci c, to group training examples before attempting concept induction. gentre is a system that has been implemented to test the performance of the method. gentre is not limited to variable-free, function-free or nonrecursive domains as many other approaches. In the paper we present results from experiments in three di erent domains and compare the performance of gentre with that of ID3 and IOU. The learned concept descriptions are consistent with training examples and have an improved classi cation accuracy relative to the original domain theory.

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تاریخ انتشار 1994