Active Learning with Statistical Models

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

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