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Modeling for optimal probability prediction

dc.contributor.authorWang, Yong
dc.contributor.authorWitten, Ian H.
dc.coverage.spatialConference held at University of NSW, Sydney, Australiaen_NZ
dc.date.accessioned2009-04-29T00:24:52Z
dc.date.available2009-04-29T00:24:52Z
dc.date.issued2002
dc.identifier.citationWang, Y. & Witten, I.H. (2002). Modeling for optimal probability prediction. In Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, July (pp. 650-657). San Francisco: Morgan Kaufmann Publishers Inc.en
dc.identifier.urihttps://hdl.handle.net/10289/2131
dc.description.abstractWe present a general modelling method for optimal probability prediction over future observations, in which model dimensionality is determined as a natural by-product. This new method yields several estimators, and we establish theoretically that they are optimal (either overall or under stated restrictions) when the number of free parameters is infinite. As a case study, we investigate the problem of fitting logistic models in finite-sample situations. Simulation results on both artificial and practical datasets are supportive.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherMorgan Kaufmann Publishers Inc.en
dc.rightsThis is the author’s version of a conference paper published in Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, July. ©2002 Authorsen
dc.subjectcomputer scienceen
dc.subjectMachine learning
dc.titleModeling for optimal probability predictionen
dc.typeConference Contributionen
dc.relation.isPartOfICMLen_NZ
pubs.begin-page650en_NZ
pubs.elements-id11786
pubs.end-page657en_NZ
pubs.finish-date2002-07-12en_NZ
pubs.place-of-publicationSan Francisco, Californiaen_NZ
pubs.start-date2002-07-08en_NZ
pubs.volumeProc. Nineteenth International Conference on Machine Learningen_NZ


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