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Support Vector Machine Applications in Terahertz Pulsed Signals Feature Sets
2007
IEEE Sensors Journal
In the past decade, terahertz radiation (T-rays) have been extensively applied within the fields of industrial and biomedical imaging, owing to their noninvasive property. Support vector machine (SVM) learning algorithms are sufficiently powerful to detect patterns hidden inside noisy biomedical measurements. This paper introduces a frequency orientation component method to extract T-ray feature sets for the application of two-and multiclass classification using SVMs. Effective discriminations
doi:10.1109/jsen.2007.908243
fatcat:37gynr22vvcqli54jzgae24z4e