Using Recurrent Networks for Dimensionality Reduction

dc.creator	Jones, Michael J.
dc.date	2004-10-20T20:23:37Z
dc.date	2004-10-20T20:23:37Z
dc.date	1992-09-01
dc.date.accessioned	2013-10-09T02:48:05Z
dc.date.available	2013-10-09T02:48:05Z
dc.date.issued	2013-10-09
dc.identifier	AITR-1396
dc.identifier	http://hdl.handle.net/1721.1/7045
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	This report explores how recurrent neural networks can be exploited for learning high-dimensional mappings. Since recurrent networks are as powerful as Turing machines, an interesting question is how recurrent networks can be used to simplify the problem of learning from examples. The main problem with learning high-dimensional functions is the curse of dimensionality which roughly states that the number of examples needed to learn a function increases exponentially with input dimension. This thesis proposes a way of avoiding this problem by using a recurrent network to decompose a high-dimensional function into many lower dimensional functions connected in a feedback loop.
dc.format	2167097 bytes
dc.format	1325986 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AITR-1396
dc.title	Using Recurrent Networks for Dimensionality Reduction

Files in this item

Files	Size	Format	View
There are no files associated with this item.