%0 Conference Paper %1 Webb90 %A Webb, G. I. %B Proceedings of the First Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW'90) %C Tokyo %D 1990 %E Motada, H. %E Mizoguchi, R. %E Boose, J. %E Gaines, B. %I IOS Press %K Learning Rule %P 219-232 %T Rule Optimisation and Theory Optimisation: Heuristic Search Strategies for Data-Driven Machine Learning %X Previous implementations of the Aq algorithm have used rule optimisation search strategies to attempt to develop optimal classification procedures. These strategies involve generating successive characteristic descriptions each of which is individually of maximal value. This is contrasted with theory optimisation search strategies which, instead, generate successive complete classification procedures from which those with the maximal value are selected. These two strategies have been applied to the domain of the diagnosis of Immunoglobulin A Nephropathy disease. The theory optimisation strategy was observed to out perform the rule optimisation strategy.

@inproceedings{Webb90, abstract = {Previous implementations of the Aq algorithm have used rule optimisation search strategies to attempt to develop optimal classification procedures. These strategies involve generating successive characteristic descriptions each of which is individually of maximal value. This is contrasted with theory optimisation search strategies which, instead, generate successive complete classification procedures from which those with the maximal value are selected. These two strategies have been applied to the domain of the diagnosis of Immunoglobulin A Nephropathy disease. The theory optimisation strategy was observed to out perform the rule optimisation strategy.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Tokyo}, audit-trail = {Reconstructed paper posted}, author = {Webb, G. I.}, biburl = {https://www.bibsonomy.org/bibtex/257292fd0cbd46f5244d70f2a3b853875/giwebb}, booktitle = {Proceedings of the First Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW'90)}, editor = {Motada, H. and Mizoguchi, R. and Boose, J. and Gaines, B.}, interhash = {6820cf58756a37d6714dec53f1fcad36}, intrahash = {57292fd0cbd46f5244d70f2a3b853875}, keywords = {Learning Rule}, location = {Kyoto, Japan}, pages = {219-232}, publisher = {IOS Press}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Rule Optimisation and Theory Optimisation: Heuristic Search Strategies for Data-Driven Machine Learning}, year = 1990 }