Logistic regression and boosting for labeled bags of instances

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dc.contributor.authorXu, Xin
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Sydney, Australiaen_NZ
dc.date.accessioned2008-11-21T03:52:26Z
dc.date.available2008-11-21T03:52:26Z
dc.date.issued2004
dc.description.abstractIn this paper we upgrade linear logistic regression and boosting to multi-instance data, where each example consists of a labeled bag of instances. This is done by connecting predictions for individual instances to a bag-level probability estimate by simple averaging and maximizing the likelihood at the bag level—in other words, by assuming that all instances contribute equally and independently to a bags label. We present empirical results for artificial data generated according to the underlying generative model that we assume, and also show that the two algorithms produce competitive results on the Musk benchmark datasets.en_US
dc.identifier.citationXu, X. & Frank, E. (2004). Logistic regression and boosting for labeled bags of instances. In H. Dai, R. Srikant, & C. Zhang (Eds.), Proceedings 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004(pp. 272-281). Berlin: Springer.en_US
dc.identifier.doi10.1007/978-3-540-24775-3_35en_US
dc.identifier.urihttps://hdl.handle.net/10289/1450
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.isPartOf8th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining Conferenceen_NZ
dc.relation.urihttp://www.springerlink.com/content/h1hvxx7w4aw302he/en_US
dc.sourcePAKDD 2004en_NZ
dc.subjectcomputer scienceen_US
dc.subjectlogistic regressionen_US
dc.subjectMachine learning
dc.titleLogistic regression and boosting for labeled bags of instancesen_US
dc.typeConference Contributionen_US
pubs.begin-page272en_NZ
pubs.elements-id14812
pubs.end-page281en_NZ
pubs.finish-date2004-05-28en_NZ
pubs.place-of-publicationBerlinen_NZ
pubs.start-date2004-05-26en_NZ
pubs.volumeLNAI 3056en_NZ
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