Nonlinear models for neural networks.

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dc.contributor.advisor Haines, Linda M.
dc.creator Brittain, Susan.
dc.date.accessioned 2011-08-03T07:15:18Z
dc.date.available 2011-08-03T07:15:18Z
dc.date.created 2000
dc.date.issued 2000
dc.identifier.uri http://hdl.handle.net/10413/3315
dc.description Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2000. en
dc.description.abstract The most commonly used applications of hidden-layer feed forward neural networks are to fit curves to regression data or to provide a surface from which a classification rule can be found. From a statistical viewpoint, the principle underpinning these networks is that of nonparametric regression with sigmoidal curves being located and scaled so that their sum approximates the data well, and the underlying mechanism is that of nonlinear regression, with the weights of the network corresponding to parameters in the regression model, and the objective function implemented in the training of the network defining the error structure. The aim ofthe present study is to use these statistical insights to critically appraise the reliability and the precision of the predicted outputs from a trained hiddenlayer feed forward neural network. en
dc.language.iso en en
dc.subject Theses--Statistics and actuarial science. en
dc.subject Neural Networks (Computer Science) en
dc.title Nonlinear models for neural networks. en
dc.type Thesis en

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