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dc.contributor.authorPfahringer, Bernhard
dc.date.accessioned2010-06-28T22:47:07Z
dc.date.available2010-06-28T22:47:07Z
dc.date.issued2010-06
dc.identifier.citationPfahringer, B. (2010). Random model trees: an effective and scalable regression method. (Working paper 03/2010). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_NZ
dc.identifier.issn1177-777X
dc.identifier.urihttps://hdl.handle.net/10289/4056
dc.description.abstractWe present and investigate ensembles of randomized model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivaling the state of the art in numeric prediction. An extensive empirical investigation shows that Random Model Trees produce predictive performance which is competitive with state-of-the-art methods like Gaussian Processes Regression or Additive Groves of Regression Trees. The training and optimization of Random Model Trees scales better than Gaussian Processes Regression to larger datasets, and enjoys a constant advantage over Additive Groves of the order of one to two orders of magnitude.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_NZ
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_NZ
dc.subjectregressionen_NZ
dc.subjectensemblesen_NZ
dc.subjectsupervised learningen_NZ
dc.subjectrandomizationen_NZ
dc.subjectMachine learning
dc.titleRandom model trees: an effective and scalable regression methoden_NZ
dc.typeWorking Paperen_NZ
uow.relation.series03/2010
pubs.elements-id54056
pubs.place-of-publicationHamilton, New Zealanden_NZ


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