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Advances in statistical learning theory present the opportunity to developstatistical models of quantum many-body systems exhibiting remarkablepredictive power. The potential of such ``theory-thin'' approaches isillustrated with the application of Support Vector Machines (SVMs) to globalprediction of nuclear properties as functions of proton and neutron numbers $Z$and $N$ across the nuclidic chart. Based on the principle of structural-riskminimization, SVMs learn from examples in the existing database of a givenproperty $Y$, automatically and optimally identify a set of ``support vectors''corresponding to representative nuclei in the training set, and approximate themapping $(Z,N) \to Y$ in terms of these nuclei. Results are reported fornuclear masses, beta-decay lifetimes, and spins/parities of nuclear groundstates. These results indicate that SVM models can match or even surpass thepredictive performance of the best conventional ``theory-thick'' global modelsbased on nuclear phenomenology.Comment: 15 pages, 1 figure, 13th International Conference on Recent Progress  in Many-Body Theories QMBT1

APPLICATION OF SUPPORT VECTOR MACHINES TO GLOBAL PREDICTION OF NUCLEAR PROPERTIES