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Sets of intelligent classifiers are applied to the near-field scan-data inorder to automatically classify the shape of radiating wirings. The supportvector machine, k-nearest neighbors algorithm, and Gaussian processclassifications are trained using the near-field radiation pattern of diverseradiating wire configurations. Leave-one-out cross-validation is used forestimating the performance of the predictive models. The output of thisresearch is a software package well-suited to be retrained based on anymeasured near-field databank to automate the identification of magnetic-type orelectric-type of the radiating coupling sources.

Machine-Learning Classification of Closed and Open Radiating Wires from Near Magnetic or Electric Field Scan Images