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Proceedings Paper

Optimization of support vector machine (SVM) for object classification
Author(s): Matthew Scholten; Neil Dhingra; Thomas T. Lu; Tien-Hsin Chao
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Paper Abstract

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data in to species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Paper Details

Date Published: 23 April 2012
PDF: 9 pages
Proc. SPIE 8398, Optical Pattern Recognition XXIII, 839806 (23 April 2012); doi: 10.1117/12.923483
Show Author Affiliations
Matthew Scholten, California State Univ. (United States)
Neil Dhingra, Univ. of Michigan (United States)
Thomas T. Lu, Jet Propulsion Lab. (United States)
Tien-Hsin Chao, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 8398:
Optical Pattern Recognition XXIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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