%0 Conference Paper %1 Webb91d %A Webb, G. I. %B Proceedings of the First Australian Workshop on Knowledge Acquisition for Knowledge-Based Systems (AKAW '91) %C Sydney %D 1991 %E Quinlan, R. %I University of Sydney Press. %K Learning Rule %P 44-52 %T Data Driven Inductive Refinement of Production Rules %X This paper presents algorithms for inductive refinement of production rules based on the DLG data-driven machine learning algorithm. These algorithms modify the input production rules with reference to a set of examples so as to ensure that all positive examples are covered and no negative examples are covered. The input production rules may either have been previously learnt by a machine learning system or be extracted from an existing expert system.

@inproceedings{Webb91d, abstract = {This paper presents algorithms for inductive refinement of production rules based on the DLG data-driven machine learning algorithm. These algorithms modify the input production rules with reference to a set of examples so as to ensure that all positive examples are covered and no negative examples are covered. The input production rules may either have been previously learnt by a machine learning system or be extracted from an existing expert system.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Sydney}, audit-trail = {Reconstructed paper posted October 2005}, author = {Webb, G. I.}, biburl = {https://www.bibsonomy.org/bibtex/23c8a0dc89dd6819a6243835211f228a3/giwebb}, booktitle = {Proceedings of the First Australian Workshop on Knowledge Acquisition for Knowledge-Based Systems (AKAW '91)}, editor = {Quinlan, R.}, interhash = {ec1bfda2ad9c171f598b4365d5da9858}, intrahash = {3c8a0dc89dd6819a6243835211f228a3}, keywords = {Learning Rule}, location = {Pokolbin, NSW, Australia}, pages = {44-52}, publisher = {University of Sydney Press.}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Data Driven Inductive Refinement of Production Rules}, year = 1991 }