Active Learning with Statistical Models

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7192

Full metadata record

DC Field	Value	Language
dc.creator	Cohn, David A.	-
dc.creator	Ghahramani, Zoubin	-
dc.creator	Jordan, Michael I.	-
dc.date	2004-10-20T20:49:20Z	-
dc.date	2004-10-20T20:49:20Z	-
dc.date	1995-03-21	-
dc.date.accessioned	2013-10-09T02:48:31Z	-
dc.date.available	2013-10-09T02:48:31Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1522	-
dc.identifier	CBCL-110	-
dc.identifier	http://hdl.handle.net/1721.1/7192	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.	-
dc.format	6 p.	-
dc.format	266098 bytes	-
dc.format	440905 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1522	-
dc.relation	CBCL-110	-
dc.subject	AI	-
dc.subject	MIT	-
dc.subject	Artificial Intelligence	-
dc.subject	active learning	-
dc.subject	queries	-
dc.subject	locally weighted regression	-
dc.subject	LOESS	-
dc.subject	mixtures of gaussians	-
dc.subject	exploration	-
dc.subject	robotics	-
dc.title	Active Learning with Statistical Models	-
Appears in Collections:	MIT Items

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