Hierarchical Mixtures of Experts and the EM Algorithm

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7206

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DC Field	Value	Language
dc.creator	Jordan, Michael I.	-
dc.creator	Jacobs, Robert A.	-
dc.date	2004-10-20T20:49:48Z	-
dc.date	2004-10-20T20:49:48Z	-
dc.date	1993-08-01	-
dc.date.accessioned	2013-10-09T02:48:33Z	-
dc.date.available	2013-10-09T02:48:33Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1440	-
dc.identifier	CBCL-083	-
dc.identifier	http://hdl.handle.net/1721.1/7206	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.	-
dc.format	29 p.	-
dc.format	190144 bytes	-
dc.format	678911 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1440	-
dc.relation	CBCL-083	-
dc.subject	supervised learning	-
dc.subject	statistics	-
dc.subject	decision trees	-
dc.subject	neuralsnetworks	-
dc.title	Hierarchical Mixtures of Experts and the EM Algorithm	-
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