Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6836

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DC Field	Value	Language
dc.creator	Resnick, Paul	-
dc.date	2004-10-20T20:00:53Z	-
dc.date	2004-10-20T20:00:53Z	-
dc.date	1989-02-01	-
dc.date.accessioned	2013-10-09T02:47:11Z	-
dc.date.available	2013-10-09T02:47:11Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1052	-
dc.identifier	http://hdl.handle.net/1721.1/6836	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components.	-
dc.format	101 p.	-
dc.format	11635658 bytes	-
dc.format	4564645 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1052	-
dc.subject	learning	-
dc.subject	explanation-based learning	-
dc.subject	model-basedstroubleshooting	-
dc.title	Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology	-
Appears in Collections:	MIT Items

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