  We have made initial studies of the potential of support vector machines
(SVM) for providing statistical models of nuclear systematics with demonstrable
predictive power. Using SVM regression and classification procedures, we have
created global models of atomic masses, beta-decay halflives, and ground-state
spins and parities. These models exhibit performance in both data-fitting and
prediction that is comparable to that of the best global models from nuclear
phenomenology and microscopic theory, as well as the best statistical models
based on multilayer feedforward neural networks.
