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<title>SVM classifier applied to the MSTAR public data set</title>
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1999
Year
EngineeringMachine LearningSupport Vector MachineImage AnalysisData SciencePattern RecognitionSupport Vector MachinesRadar Signal ProcessingStationary Target AcquisitionSvm ClassifierMachine VisionAutomatic Target RecognitionSynthetic Aperture RadarRadar ApplicationComputer ScienceSignal ProcessingComputer VisionRadarData ClassificationRadar Image ProcessingClassifier System
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.