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dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorLeschi, Claire
dc.contributor.authorReutemann, Peter
dc.coverage.spatialConference held at Nanjing, Chinaen_NZ
dc.identifier.citationPfahringer, B., Leschi, C. & Reutemann, P.(2007). Scaling up semi-supervised learning: An efficient and effective LLGC variant. In Z.-H. Zhou, H. Li & Q. Yang(Eds.), Proceedings 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007.(pp. 236-247). Berlin: Springer.en_US
dc.description.abstractDomains like text classification can easily supply large amounts of unlabeled data, but labeling itself is expensive. Semi- supervised learning tries to exploit this abundance of unlabeled training data to improve classification. Unfortunately most of the theoretically well-founded algorithms that have been described in recent years are cubic or worse in the total number of both labeled and unlabeled training examples. In this paper we apply modifications to the standard LLGC algorithm to improve efficiency to a point where we can handle datasets with hundreds of thousands of training data. The modifications are priming of the unlabeled data, and most importantly, sparsification of the similarity matrix. We report promising results on large text classification problems.en_US
dc.publisherSpringer, Berlinen_US
dc.sourcePAKDD 2007en_NZ
dc.subjectcomputer scienceen_US
dc.subjectsemi-supervised learningen_US
dc.subjectLLGC algorithmen_US
dc.subjectMachine learning
dc.titleScaling up semi-supervised learning: An efficient and effective LLGC varianten_US
dc.typeConference Contributionen_US
dc.relation.isPartOfProc 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Miningen_NZ
pubs.volumeLNCS 4426en_NZ

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