  Sets of intelligent classifiers are applied to the near-field scan-data in
order to automatically classify the shape of radiating wirings. The support
vector machine, k-nearest neighbors algorithm, and Gaussian process
classifications are trained using the near-field radiation pattern of diverse
radiating wire configurations. Leave-one-out cross-validation is used for
estimating the performance of the predictive models. The output of this
research is a software package well-suited to be retrained based on any
measured near-field databank to automate the identification of magnetic-type or
electric-type of the radiating coupling sources.
