On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6624

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DC Field	Value	Language
dc.creator	Niyogi, Partha	-
dc.creator	Girosi, Federico	-
dc.date	2004-10-08T20:34:39Z	-
dc.date	2004-10-08T20:34:39Z	-
dc.date	1994-02-01	-
dc.date.accessioned	2013-10-09T02:46:10Z	-
dc.date.available	2013-10-09T02:46:10Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1467	-
dc.identifier	http://hdl.handle.net/1721.1/6624	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	In this paper, we bound the generalization error of a class of Radial Basis Function networks, for certain well defined function learning tasks, in terms of the number of parameters and number of examples. We show that the total generalization error is partly due to the insufficient representational capacity of the network (because of its finite size) and partly due to insufficient information about the target function (because of finite number of samples). We make several observations about generalization error which are valid irrespective of the approximation scheme. Our result also sheds light on ways to choose an appropriate network architecture for a particular problem.	-
dc.format	261921 bytes	-
dc.format	1092393 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1467	-
dc.title	On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions	-
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