Mason Archival Repository Service

The Use of Compound Attributes in AQ Learning

Show simple item record

dc.contributor.author Wojtusiak, Janusz
dc.contributor.author Michalski, Ryszard S.
dc.date.accessioned 2006-11-03T18:17:36Z
dc.date.available 2006-11-03T18:17:36Z
dc.date.issued 2006-06 en_US
dc.identifier.citation Wojtusiak, J. and Michalski, R. S., "The Use of Compound Attributes in AQ Learning," Proceedings of the Intelligent Information Processing and Web Mining Conference, IIPWM 06, Ustron, Poland, June 19-22, 2006. en_US
dc.identifier.uri https://hdl.handle.net/1920/1498
dc.description.abstract Compound attributes are named groups of attributes that have been introduced in Attributional Calculus (AC) to facilitate learning descriptions of objects whose components are characterized by different subsets of attributes. The need for such descriptions appears in many practical applications. A method for handling compound attributes in AQ learning and testing is described and illustrated by examples.
dc.description.sponsorship This research was conducted in the GMU Machine Learning and Inference Laboratory, whose research activities are supported in part by the National Science Foundation Grants No. IIS 9906858 and IIS 0097476. en_US
dc.format.extent 1566 bytes
dc.format.extent 335913 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 06-4 en_US
dc.title The Use of Compound Attributes in AQ Learning en_US
dc.type Presentation en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search MARS


Browse

My Account

Statistics