Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.3/5507

Full metadata record

DC Field	Value	Language
dc.creator	Mukherjee, Sayan	-
dc.creator	Niyogi, Partha	-
dc.creator	Poggio, Tomaso	-
dc.creator	Rifkin, Ryan	-
dc.date	2004-08-31T18:12:01Z	-
dc.date	2004-08-31T18:12:01Z	-
dc.date	2002-12-01	-
dc.date.accessioned	2013-10-09T02:39:53Z	-
dc.date.available	2013-10-09T02:39:53Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-2002-024	-
dc.identifier	CBCL-223	-
dc.identifier	http://hdl.handle.net/1721.3/5507	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Solutions of learning problems by Empirical Risk Minimization (ERM) need to be consistent, so that they may be predictive. They also need to be well-posed, so that they can be used robustly. We show that a statistical form of well-posedness, defined in terms of the key property of L-stability, is necessary and sufficient for consistency of ERM.	-
dc.description	revised July 2003	-
dc.format	24 p.	-
dc.format	1854466 bytes	-
dc.format	400508 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-2002-024	-
dc.relation	CBCL-223	-
dc.subject	AI	-
dc.subject	Theory of Learning	-
dc.subject	Great Discoveries	-
dc.subject	Consistency	-
dc.subject	ERM	-
dc.subject	Stability	-
dc.title	Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization	-
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