Modeling Nuclear Properties with Support Vector Machines

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dc.creatorLi, Haochen
dc.creatorClark, J. W.
dc.creatorMavrommatis, E.
dc.creatorAthanassopoulos, S.
dc.creatorGernoth, K. A.
dc.date2005-06-28
dc.date2006-03-21
dc.date.accessioned2026-07-07T06:43:17Z
dc.date.available2026-07-07T06:43:17Z
dc.descriptionWe 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.
dc.description15 pages; website with latest results added
dc.identifierhttps://arxiv.org/abs/nucl-th/0506080
dc.identifierhttp://arxiv.org/abs/nucl-th/0506080
dc.identifierCondensed Matter Theories, Vol. 20, edited by J. W. Clark, R. M. Panoff, and H. Li (Nova Science Publishers, Hauppauge, NY, 2005)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/102358
dc.subjectNuclear Theory
dc.titleModeling Nuclear Properties with Support Vector Machines
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