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Proceedings Paper

Fusion rule estimation using vector space methods
Author(s): Nageswara S. V. Rao
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Paper Abstract

In a system of N sensors, the sensor (formula available in paper) The problem is to estimate a fusion rule (formula available in paper), based on the sample, such that the expected square error is minimized over a family of functions F that constitute a vector space. The function f* that minimizes the expected error cannot be computed since the underlying densities are unknown, and only an approximation f to f* is feasible. We estimate the sample size sufficient to ensure that f provides a close approximation to f* with a high probability. The advantages of vector space methods are two-fold: (1) the sample size estimate is a simple function of the dimensionality of F, and (2) the estimate f can be easily computed by well-known least square methods in polynomial time. The results are applicable to the classical potential function methods and also (to a recently proposed) special class of sigmoidal feedforward neural networks.

Paper Details

Date Published: 16 June 1997
PDF: 6 pages
Proc. SPIE 3067, Sensor Fusion: Architectures, Algorithms, and Applications, (16 June 1997); doi: 10.1117/12.276123
Show Author Affiliations
Nageswara S. V. Rao, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 3067:
Sensor Fusion: Architectures, Algorithms, and Applications
Belur V. Dasarathy, Editor(s)

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