Application of support vector machines to global prediction of nuclear properties

Advances in statistical learning theory present the opportunity to develop statistical models of quantum many-body systems exhibiting remarkable predictive power. The potentiall of such "theory-thin" approaches is illustrated with the application of Support Vector Machines (SVMs) to global prediction of nuclear properties as functions of proton and neutron numbers Z and N across the. nuclidic chart. Based on the principle of structural-risk minimization, SVMs learn from examples in the existing database of a given property Y, automatically and optimally identify a set of ",support vectors" corresponding to representative nuclei in the training set, and approximate the mapping (Z, N) -> Y in terms of these nuclei. Results are. reported for nuclear masses, beta-decay lifetimes, and spins/parities of nuclear ground states. These results indicate that SVM models can match or even surpass the predictive. performance of the best conventional "theory-thick" global models based on nuclear phenomenology.