Show simple item record  

dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorSchmidberger, Gabi
dc.coverage.spatialConference held at Adelaide, Australiaen_NZ
dc.identifier.citationPfahringer, B., Holmes, G. & Schmidberger, G.(2001). Wrapping boosters against noise. In Proceedings of 14th Australian Joint Conference on Artificial Intelligence Adelaide, Australia, December 10–14, 2001(pp.593-605). Berlin: Springer.en_US
dc.description.abstractWrappers have recently been used to obtain parameter optimizations for learning algorithms. In this paper we investigate the use of a wrapper for estimating the correct number of boosting ensembles in the presence of class noise. Contrary to the naive approach that would be quadratic in the number of boosting iterations, the incremental algorithm described is linear. Additionally, directly using the k-sized ensembles generated during k-fold cross-validation search for prediction usually results in further improvements in classification performance. This improvement can be attributed to the reduction of variance due to averaging k ensembles instead of using only one ensemble. Consequently, cross-validation in the way we use it here, termed wrapping, can be viewed as yet another ensemble learner similar in spirit to bagging but also somewhat related to stacking.en_US
dc.publisherSpringer, Berlinen_US
dc.sourceAI 2001en_NZ
dc.subjectcomputer scienceen_US
dc.subjectMachine learningen_US
dc.titleWrapping boosters against noiseen_US
dc.typeConference Contributionen_US
dc.relation.isPartOfAdvances in Artificial Intelligence: 14th Australian Joint Conference on Artificial Intelligenceen_NZ
pubs.volumeLNCS 2256en_NZ

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record