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Proceedings Paper

Application of the optimal brain surgeon pruning strategy to a real-aperture radar detection algorithm
Author(s): Kenneth I. Ranney; Hiralal Khatri; Peter Alexander
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Paper Abstract

We have applied the Optimal Brain Surgeon (OBS) pruning strategy to a polynomial discriminator in order to reduce the number of coefficients it employs. The polynomial discriminator multiplies various combinations of test features by the respective coefficients and then sums the products to obtain a discriminant that is compared to a threshold. The test features are derived from the radar data associated with the cell under test, while the coefficients are determined a priori by minimizing the mean-squared error (MSE) between the actual and the desired value of the discriminant over the training set. The OBS pruning strategy examines the Hessian matrix of a network's error surface-- derived from the training data--to determine which coefficients can be eliminated without adversely affecting the MSE. Besides simplifying the network, such a reduction may also allow for improved network performance when an unseen test data is input. We present the application of the OBS pruning strategy to reduce the dimensionality of a polynomial discriminator and show that the reduction in dimensionality does not adversely affect performance.

Paper Details

Date Published: 20 July 1999
PDF: 10 pages
Proc. SPIE 3704, Radar Sensor Technology IV, (20 July 1999); doi: 10.1117/12.354591
Show Author Affiliations
Kenneth I. Ranney, Army Research Lab. (United States)
Hiralal Khatri, Army Research Lab. (United States)
Peter Alexander, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 3704:
Radar Sensor Technology IV
Robert Trebits; James L. Kurtz, Editor(s)

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