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Proceedings Paper

From hyperplanes to large-margin classifiers: applications of SAR ATR
Author(s): Qun Zhao; Jose C. Principe; Dongxin Xu
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Paper Abstract

In this paper, the structural risk minimization (SRM) criterion is employed to train a large margin classifier, the support vector machine (SVM). Its relative performance is compared with traditional classifiers employing hyperplanes against a realistic difficult problem, the synthetic aperture radar (SAR) automatic target recognition (ATR). In most pattern recognition applications, the task is to perform classification into a fixed number of classes. However, in some practical cases, such as ATR, one also needs to carry out a reliable pattern rejection. Experimental results showed that the SVM with the Gaussian kernels performs well in target recognition. Moreover, the SVM is able to form a local or 'bounded' decision region that presents better rejection to confusers.

Paper Details

Date Published: 24 August 1999
PDF: 9 pages
Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359940
Show Author Affiliations
Qun Zhao, Univ. of Florida (United States)
Jose C. Principe, Univ. of Florida (United States)
Dongxin Xu, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 3718:
Automatic Target Recognition IX
Firooz A. Sadjadi, Editor(s)

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