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dc.contributor.authorZhong, Mingyu
dc.contributor.authorCoggeshall, Dave
dc.contributor.authorGhaneie, Ehsan
dc.contributor.authorPope, Thomas
dc.contributor.authorRivera, Mark
dc.contributor.authorGeorgiopoulos, Michael
dc.contributor.authorAnagnostopoulos, Georgios C.
dc.contributor.authorMollaghasemi, Mansooreh
dc.contributor.authorRichie, Samuel
dc.date.accessioned2017-12-13T21:28:48Z
dc.date.available2017-12-13T21:28:48Z
dc.date.issued2007-10
dc.identifier.citationMingyu, Z., Coggeshall, D., Ghaneie, E., Pope, T., Rivera, M., Georgiopoulos, M., Anagnostopoulos, G.C., Mollaghasemi, M., Richie, S. Gap-based estimation: Choosing the smoothing parameters for probabilistic and general regression neural networks (2007) Neural Computation, 19 (10), pp. 2840-2864. Cited 9 times.en_US
dc.identifier.urihttp://hdl.handle.net/11141/2230
dc.descriptionarticle, artificial neural network, cluster analysis, statistical modelen_US
dc.description.abstractProbabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by crossvalidation or clustering. In this article, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of © 2007 Massachusetts Institute of Technology the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy.en_US
dc.language.isoen_USen_US
dc.rights© 2007 Massachusetts Institute of Technologyen_US
dc.rights.urihttp://www.mitpressjournals.org/for_authors#authorpostingen_US
dc.titleGap-based estimation: Choosing the smoothing parameters for probabilistic and general regression neural networksen_US
dc.typeOtheren_US
dc.identifier.doi10.1162/neco.2007.19.10.2840


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