Locally linear approximation for Kernel methods : the Railway Kernel

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dc.contributor.author González, Javier
dc.contributor.author Muñoz, Alberto
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2008-12-22T13:52:04Z
dc.date.available 2008-12-22T13:52:04Z
dc.date.issued 2008-12
dc.identifier.uri http://hdl.handle.net/10016/3380
dc.description.abstract In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capability of the proposed kernel is higher than the obtained using RBF kernels. Experimental work is shown to support the theoretical issues.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 08-24
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Support vector machines
dc.subject.other Kernel Methods
dc.subject.other Classification problems
dc.title Locally linear approximation for Kernel methods : the Railway Kernel
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws087024
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