Gaussian process (GP) regression is proposed as a
structured supervised learning alternative to neural networks for the modeling of CPW-fed slot antenna input characteristics. A Gaussian process is a stochastic process and entails the generalization of the Gaussian probability distribution to functions. Standard GP regression
is applied to modeling S11 against frequency of a CPW-fed second-resonant slot dipole, while an approximate method for large datasets is applied to an ultrawideband (UWB) slot with U-shaped tuning stub. A challenging problem given the highly non-linear underlying function that maps tunable geometry variables and frequency to S11= input impedance. Predictions using large test data sets yielded results of an accuracy comparable to the target moment-method-based full-wave simulations, with normalized root mean squared errors of 0.50% for the slot dipole, and below 1.8% for the UWB antenna. The GP methodology has various inherent benefits, including the need to learn only a handful of (hyper) parameters, and training errors that are
effectively zero for noise-free observations. GP regression would be eminently suitable for integration in antenna design algorithms as a
fast substitute for computationally intensive full-wave analysis.