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The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide

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dc.contributor.author Michalski, Ryszard S.
dc.contributor.author Kaufman, Kenneth A.
dc.date.accessioned 2006-11-03T18:14:33Z
dc.date.available 2006-11-03T18:14:33Z
dc.date.issued 2000-03 en_US
dc.identifier.citation Kaufman, K. and Michalski, R. S., "The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide," Reports of the Machine Learning and Inference Laboratory, MLI 00-3, George Mason University, Fairfax, VA, 2000. en_US
dc.identifier.uri https://hdl.handle.net/1920/1459
dc.description.abstract This report is a comprehensive user's guide for AQ18, an environment for natural induction, machine learning and knowledge discovery. By natural induction is meant a form of inductive inference which strives to induce data descriptions that are most natural and comprehensible to people. This feature is achieved by employing a highly expressive description language (attributional calculus). Along with a learning for determining attributional rulesets from examples, or for incrementally improving the previously learned rulesets through new examples, AQ18 also incorporates a ruleset testing module (ATEST) and a module for selecting the best attributes for a given learning problem (PROMISE).
dc.format.extent 1967 bytes
dc.format.extent 597732 bytes
dc.format.extent 187664 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 00-3 en_US
dc.subject Machine learning en_US
dc.subject data mining en_US
dc.subject inductive inference en_US
dc.subject learning from examples en_US
dc.title The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide en_US
dc.type Technical report en_US


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