Actions for Fusion rule estimation using vector space methods [electronic resource].

Email RIS file
Bookmark
Report an Issue

Fusion rule estimation using vector space methods [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Energy Research, 1997.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
8 pages : digital, PDF file
Additional Creators
Oak Ridge National Laboratory, United States. Department of Energy. Office of Energy Research, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online

Availability

I Want It
I Want It

Finding items...

Restrictions on Access
Free-to-read Unrestricted online access
Summary
In a system of N sensors, the sensor S{sub j}, j = 1, 2 .... N, outputs Y{sup (j)} ∈ ℜ, according to an unknown probability distribution P{sub (Y(j) /X)}, corresponding to input X ∈ [0, 1]. A training n-sample (X₁, Y₁), (X₂, Y₂), ..., (X{sub n}, Y{sub n}) is given where Y{sub i} = (Y{sub i}{sup (1)}, Y{sub i}{sup (2)}, . . . , Y{sub i}{sup N}) such that Y{sub i}{sup (j)} is the output of S{sub j} in response to input X{sub i}. The problem is to estimate a fusion rule f : ℜ{sup N} → [0, 1], based on the sample, such that the expected square error is minimized over a family of functions Y 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: (a) the sample size estimate is a simple function of the dimensionality of F, and (b) 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.
Report Numbers
E 1.99:conf-970465--18
conf-970465--18
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
05/01/1997.
"conf-970465--18"
"DE97006006"
SPIE international conference, Orlando, FL (United States), 21-25 Apr 1997.
Rao, N.S.V.
Funding Information
AC05-96OR22464
View MARC record | catkey: 14352750