Advanced Search
DSpace@MIT

Neural Networks

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.author Jordan, Michael I. en_US
dc.contributor.author Bishop, Christopher M. en_US
dc.date.accessioned 2004-10-20T20:49:11Z
dc.date.available 2004-10-20T20:49:11Z
dc.date.issued 1996-03-13 en_US
dc.identifier.other AIM-1562 en_US
dc.identifier.other CBCL-131 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/7186
dc.description.abstract 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. en_US
dc.format.extent 26 p. en_US
dc.format.extent 372415 bytes
dc.format.extent 583775 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries AIM-1562 en_US
dc.relation.ispartofseries CBCL-131 en_US
dc.subject AI en_US
dc.subject MIT en_US
dc.subject Artificial Intelligence en_US
dc.subject neural networks en_US
dc.subject learning en_US
dc.subject graphical models en_US
dc.subject machine learning en_US
dc.subject pattern recognition en_US
dc.subject statistical learning theory en_US
dc.title Neural Networks en_US


Files in this item

Name Size Format Description
AIM-1562.ps 363.6Kb Postscript
AIM-1562.pdf 570.0Kb PDF

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage