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dc.contributor.advisorBaras, J.en_US
dc.contributor.authorLaVigna, Anthonyen_US
dc.date.accessioned2007-05-23T09:46:37Z
dc.date.available2007-05-23T09:46:37Z
dc.date.issued1990en_US
dc.identifier.urihttp://hdl.handle.net/1903/5027
dc.description.abstractIn this thesis we study several properties of Learning Vector Quantization. LVQ is a nonparametric detection scheme proposed in the neural network community by Kohonen. We examine it in detail, both theoretically and experimentally, to determine its properties as a nonparametric classifier. In particular, we study the convergence of the parameter adjustment rule in LVQ, we present a modification to LVQ which results in improving the convergence of the algorithms, we show that LVQ performs as well as other classifiers on two sets of simulations, and we show that the classification error associated with LVQ can be made arbitrarily small.en_US
dc.format.extent4064560 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; PhD 1990-1en_US
dc.subjectSystems Integrationen_US
dc.titleNonparametric Classification Using Learning Vector Quantizationen_US
dc.typeDissertationen_US
dc.contributor.departmentISRen_US


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