Measure Fields for Function Approximation

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

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