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Proceedings Paper

SVM classifier applied to the MSTAR public data set
Author(s): Michael Lee Bryant; Frederick D. Garber
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Paper Abstract

Support vector machines (SVM) are one of the most recent tools to be developed from research in statistical learning theory. The foundations of SVM were developed by Vapnik, and are gaining popularity within the learning theory community due to many attractive features and excellent demonstrated performance. However, SVM have not yet gained popularity within the synthetic aperture radar (SAR) automatic target recognition (ATR) community. The purpose of this paper is to introduce the concepts of SVM and to benchmark its performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set.

Paper Details

Date Published: 13 August 1999
PDF: 6 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357652
Show Author Affiliations
Michael Lee Bryant, Air Force Research Lab. (United States)
Frederick D. Garber, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 3721:
Algorithms for Synthetic Aperture Radar Imagery VI
Edmund G. Zelnio, Editor(s)

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