Measure Fields for Function Approximation

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7211

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DC Field	Value	Language
dc.creator	Marroquin, Jose L.	-
dc.date	2004-10-20T20:49:55Z	-
dc.date	2004-10-20T20:49:55Z	-
dc.date	1993-06-01	-
dc.date.accessioned	2013-10-09T02:48:34Z	-
dc.date.available	2013-10-09T02:48:34Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1433	-
dc.identifier	CBCL-091	-
dc.identifier	http://hdl.handle.net/1721.1/7211	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.	-
dc.format	21 p.	-
dc.format	2521920 bytes	-
dc.format	1964059 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1433	-
dc.relation	CBCL-091	-
dc.subject	function approximation	-
dc.subject	classification	-
dc.subject	neural networks	-
dc.title	Measure Fields for Function Approximation	-
Appears in Collections:	MIT Items

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