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Study of the generalization capability versus training in backpropagation neural networks

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dc.contributor.author Dalianis, PJ en
dc.contributor.author Tzafestas, SG en
dc.contributor.author Anthopoulos, G en
dc.date.accessioned 2014-03-01T02:40:58Z
dc.date.available 2014-03-01T02:40:58Z
dc.date.issued 1993 en
dc.identifier.issn 08843627 en
dc.identifier.uri http://hdl.handle.net/123456789/30297
dc.subject backpropagation en
dc.subject backpropagation neural network en
dc.subject Dynamic Properties en
dc.subject Energy Function en
dc.subject Generalization Capability en
dc.subject.other Algorithms en
dc.subject.other Approximation theory en
dc.subject.other Degrees of freedom (mechanics) en
dc.subject.other Learning systems en
dc.subject.other Mathematical models en
dc.subject.other Numerical analysis en
dc.subject.other Pattern recognition en
dc.subject.other Performance en
dc.subject.other Plasticity en
dc.subject.other Probability en
dc.subject.other Backpropagation neural networks en
dc.subject.other Energy function en
dc.subject.other Overtraining phenomenon en
dc.subject.other Probability density function en
dc.subject.other Neural networks en
dc.title Study of the generalization capability versus training in backpropagation neural networks en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ICSMC.1993.390760 en
heal.identifier.secondary http://dx.doi.org/10.1109/ICSMC.1993.390760 en
heal.publicationDate 1993 en
heal.abstract In this paper, the phenomenon of overtraining in backpropagation neural networks is discussed. The relationships between network size, training set size and generalization capabilities are examined. An extension to an existing algorithm of backpropagation is described. The extended algorithm provides a new energy function and its advantages, such as improved plasticity and performance along with its dynamic properties, are explained. The algorithm is applied to some common problems and simulation results are presented and discussed. en
heal.publisher Publ by IEEE, Piscataway, NJ, United States en
heal.journalName Proceedings of the IEEE International Conference on Systems, Man and Cybernetics en
dc.identifier.doi 10.1109/ICSMC.1993.390760 en
dc.identifier.volume 4 en
dc.identifier.spage 485 en
dc.identifier.epage 490 en


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