Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6014

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DC Field	Value	Language
dc.creator	Poggio, Tomaso	-
dc.creator	Girosi, Federico	-
dc.date	2004-10-04T14:35:52Z	-
dc.date	2004-10-04T14:35:52Z	-
dc.date	1990-04-01	-
dc.date.accessioned	2013-10-09T02:42:25Z	-
dc.date.available	2013-10-09T02:42:25Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1167	-
dc.identifier	http://hdl.handle.net/1721.1/6014	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	The theory developed in Poggio and Girosi (1989) shows the equivalence between regularization and a class of three-layer networks that we call regularization networks or Hyper Basis Functions. These networks are also closely related to the classical Radial Basis Functions used for interpolation tasks and to several pattern recognition and neural network algorithms. In this note, we extend the theory by defining a general form of these networks with two sets of modifiable parameters in addition to the coefficients $c_\\ alpha$: moving centers and adjustable norm- weight.	-
dc.format	18 p.	-
dc.format	2271885 bytes	-
dc.format	901116 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1167	-
dc.subject	learning networks	-
dc.subject	regularization	-
dc.title	Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering	-
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