Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results
| dc.contributor.author | Cervone, Guido | |
| dc.contributor.author | Michalski, Ryszard S. | |
| dc.date.accessioned | 2006-11-03T18:17:19Z | |
| dc.date.available | 2006-11-03T18:17:19Z | |
| dc.date.issued | 2002-06 | |
| dc.description.abstract | The paper describes recent results from developing and testing LUS methodology for user modeling. LUS employs AQ learning for automatically creating user models from datasets representing activities of computer users. The datasets are stored in a relational database and employed in the learning process through an SQL-style command that automatically executes the AQ20 rule learning program and generates user models. The models are in the form of attributional rulesets that are more expressive than conventional decision rules, and are easy to interpret and understand. Early experimental results from the testing of the LUS method gave highly encouraging results. | |
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| dc.format.mimetype | text/xml | |
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| dc.identifier.citation | Cervone, G. and Michalski, R. S., "Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results," Proceedings of the IIS-02 Eleventh International Symposium on Intelligent Information Systems, Sopot, Poland, June, 2002. | |
| dc.identifier.uri | https://hdl.handle.net/1920/1479 | |
| dc.language.iso | en_US | |
| dc.relation.ispartofseries | P 02-3 | |
| dc.subject | User modeling | |
| dc.subject | Computer intrusion detection | |
| dc.subject | Machine learning | |
| dc.subject | AQ learning | |
| dc.subject | Inductive databases | |
| dc.title | Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results | |
| dc.type | Article |