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dc.contributorThe Pennsylvania State University CiteSeerX Archives
dc.contributor.authorChristos Dimitrakakis
dc.contributor.authorSamy Bengio
dc.date.accessioned2019-10-23T22:33:11Z
dc.date.available2019-10-23T22:33:11Z
dc.date.created2017-01-05 00:25
dc.date.issued2010-10-13
dc.identifieroai:CiteSeerX.psu:10.1.1.174.662
dc.identifierhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.662
dc.identifier.urihttp://hdl.handle.net/20.500.12424/779584
dc.description.abstractEnsemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put forward. The effectiveness of this approach for online learning is demonstrated by experimental results on several UCI benchmark databases.
dc.format.mediumapplication/pdf
dc.languageen
dc.language.isoeng
dc.rightsMetadata may be used without restrictions as long as the oai identifier remains attached to it.
dc.titleOnline Policy Adaptation for Ensemble Classifiers
dc.typetext
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:10372407
ge.identifier.permalinkhttps://www.globethics.net/gel/10372407
ge.lastmodificationdate2017-01-05 00:25
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid148934
ge.oai.repositoryid54
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.662
ge.linkhttp://www.idiap.ch/ftp/reports/2003/dimitrakakis-idiap-rr-03-69.pdf


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