Application Of Support Vector Machines To Global Prediction Of Nuclear Properties

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dc.creatorClark, John W.
dc.creatorLi, Haochen
dc.date2006-03-12
dc.date.accessioned2026-07-07T10:46:56Z
dc.date.available2026-07-07T10:46:56Z
dc.descriptionAdvances in statistical learning theory present the opportunity to develop statistical models of quantum many-body systems exhibiting remarkable predictive power. The potential 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 $(Z,N) \to Y$ 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.
dc.description15 pages, 1 figure, 13th International Conference on Recent Progress in Many-Body Theories QMBT13
dc.identifierhttps://arxiv.org/abs/nucl-th/0603037
dc.identifierhttp://arxiv.org/abs/nucl-th/0603037
dc.identifierInt.J.Mod.Phys.B20:5015-5029,2006
dc.identifierdoi:10.1142/S0217979206036053
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/183405
dc.subjectNuclear Theory
dc.titleApplication Of Support Vector Machines To Global Prediction Of Nuclear Properties
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