The Use of Compound Attributes in AQ Learning

Simple item page
dc.contributor.authorWojtusiak, Janusz
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:36Z
dc.date.available2006-11-03T18:17:36Z
dc.date.issued2006-06
dc.description.abstractCompound 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.sponsorshipThis 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.
dc.format.extent1566 bytes
dc.format.extent335913 bytes
dc.format.mimetypetext/xml
dc.format.mimetypeapplication/pdf
dc.identifier.citationWojtusiak, 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.
dc.identifier.urihttps://hdl.handle.net/1920/1498
dc.language.isoen_US
dc.relation.ispartofseriesP 06-4
dc.titleThe Use of Compound Attributes in AQ Learning
dc.typePresentation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
06-04.pdf
Size:
328.04 KB
Format:
Adobe Portable Document Format
Download

Collections

Machine Learning and Inference Laboratory, College of Public Health