Neural Networks

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

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