The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide
| 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 | |
| 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.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. | |
| dc.identifier.uri | https://hdl.handle.net/1920/1459 | |
| dc.language.iso | en_US | |
| dc.relation.ispartofseries | P 00-3 | |
| dc.subject | Machine learning | |
| dc.subject | Data mining | |
| dc.subject | Inductive inference | |
| dc.subject | Learning from examples | |
| dc.title | The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide | |
| dc.type | Technical report |