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Optimization of Support Vector Machine (SVM) for Object ClassificationThe Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into 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.
Document ID
Document Type
Conference Paper
External Source(s)
Scholten, Matthew
(California State Univ. Long Beach, CA, United States)
Dhingra, Neil
(Michigan Univ. MI, United States)
Lu, Thomas T.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Chao, Tien-Hsin
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 27, 2013
Publication Date
April 27, 2012
Subject Category
Meeting Information
Meeting: SPIE Symposium on Defense, Security, and Sensing
Location: Baltimore, MD
Country: United States
Start Date: April 23, 2012
End Date: April 27, 2012
Distribution Limits
support vector machice (SVM)
Automatic Target Recognition (ATR)
neural network
false alarm reduction

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