The Informational Complexity of Learning from Examples

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7069

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DC Field	Value	Language
dc.creator	Niyogi, Partha	-
dc.date	2004-10-20T20:28:05Z	-
dc.date	2004-10-20T20:28:05Z	-
dc.date	1996-09-01	-
dc.date.accessioned	2013-10-09T02:48:09Z	-
dc.date.available	2013-10-09T02:48:09Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-1587	-
dc.identifier	http://hdl.handle.net/1721.1/7069	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in the principles and parameters framework of modern linguistic theory. These problems are analyzed from the perspective of computational learning theory and certain unifying perspectives emerge.	-
dc.format	3260099 bytes	-
dc.format	3332017 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-1587	-
dc.title	The Informational Complexity of Learning from Examples	-
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