Show simple item record  

dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorReutemann, Peter
dc.contributor.authorMayo, Michael
dc.coverage.spatialConference held at Australiaen_NZ
dc.date.accessioned2008-11-28T00:19:28Z
dc.date.available2008-11-28T00:19:28Z
dc.date.issued2005
dc.identifier.citationPfahringer, B., Reutemann, P., Mayo, M. (2005). A novel two stage scheme utilizing the test set for model selection in text classification. Paper presented at the 18th Australian Joint Conference on Artificial Intelligence, University of Technology, Sydney, Australia, December 5-9, 2005.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1487
dc.description.abstractText classification is a natural application domain for semi-supervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel simple two stage scheme based on dagging which allows for utilizing the test set in model selection. The dagging ensemble can also be used by itself instead of the original classifier. We evaluate the performance of a meta classifier choosing between various base learners and their respective dagging ensembles. The selection process seems to perform robustly especially for small percentages of available labels for training.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Technology, Sydneyen_NZ
dc.subjectcomputer scienceen_US
dc.subjecttext classificationen_US
dc.subjectmodel selectionen_US
dc.subjectMachine learning
dc.titleA novel two stage scheme utilizing the test set for model selection in text classificationen_US
dc.typeConference Contributionen_US
dc.relation.isPartOfThe 18th Autralian Joint Conference on Artificial Intelligenceen_NZ
pubs.begin-page60en_NZ
pubs.elements-id16107
pubs.end-page65en_NZ
pubs.finish-date2005-12-09en_NZ
pubs.start-date2005-12-05en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record