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dc.contributor.authorMayo, Michael
dc.coverage.spatialConference held at Hong Kong, Chinaen_NZ
dc.date.accessioned2008-11-18T03:26:56Z
dc.date.available2008-11-18T03:26:56Z
dc.date.issued2007
dc.identifier.citationMayo M. (2007). Random convolution ensembles. In Advances in Multimedia Information Processing – PCM 2007, 8th Pacific Rim Conference on Multimedia, Hong Kong, China, December 11-14, 2007, Proceedings (pp. 216-225). Berlin: Springer.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1379
dc.description.abstractA novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.urihttp://www.springerlink.com/content/65th7863q1116158/en_US
dc.sourcePCM 2007en_NZ
dc.subjectcomputer scienceen_US
dc.subjectimage classificationen_US
dc.subjectrandom convolutionen_US
dc.subjectpedestrian detectionen_US
dc.subjectMachine learning
dc.titleRandom convolution ensemblesen_US
dc.typeConference Contributionen_US
dc.identifier.doi10.1007/978-3-540-77255-2_24en_US
dc.relation.isPartOfAdvances in Multimedia Information Processing: 8th Pacific Rim Conference on Multimediaen_NZ
pubs.begin-page216en_NZ
pubs.elements-id17702
pubs.end-page225en_NZ
pubs.finish-date2007-12-14en_NZ
pubs.start-date2007-12-11en_NZ
pubs.volumeLNCS 4810en_NZ


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