Neural Networks

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7186

Full metadata record

DC Field	Value	Language
dc.creator	Jordan, Michael I.	-
dc.creator	Bishop, Christopher M.	-
dc.date	2004-10-20T20:49:11Z	-
dc.date	2004-10-20T20:49:11Z	-
dc.date	1996-03-13	-
dc.date.accessioned	2013-10-09T02:48:29Z	-
dc.date.available	2013-10-09T02:48:29Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1562	-
dc.identifier	CBCL-131	-
dc.identifier	http://hdl.handle.net/1721.1/7186	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.	-
dc.format	26 p.	-
dc.format	372415 bytes	-
dc.format	583775 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1562	-
dc.relation	CBCL-131	-
dc.subject	AI	-
dc.subject	MIT	-
dc.subject	Artificial Intelligence	-
dc.subject	neural networks	-
dc.subject	learning	-
dc.subject	graphical models	-
dc.subject	machine learning	-
dc.subject	pattern recognition	-
dc.subject	statistical learning theory	-
dc.title	Neural Networks	-
Appears in Collections:	MIT Items

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