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dc.contributor.authorMartín Muñoz, Mario
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-11-30T17:56:36Z
dc.date.available2016-11-30T17:56:36Z
dc.date.issued2002-02
dc.identifier.citationMartin, M. "On-line support vector machines for function approximation". 2002.
dc.identifier.urihttp://hdl.handle.net/2117/97569
dc.description.abstractThis paper describes an on-line method for building epsilon-insensitive support vector machines for regression as described in (Vapnik, 1995). The method is an extension of the method developed by (Cauwenberghs & Poggio, 2000) for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like quadratic programming, but they are obtained more quickly and allow the incremental addition of new points, removal of existing points and update of target values for existing data. This development opens the application of SVM regression to areas such as on-line prediction of temporal series or generalization of value functions in reinforcement learning.
dc.format.extent11 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-02-11-R
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.otherOn-line support
dc.subject.otherVector machines
dc.subject.otherFunction approximation
dc.subject.otherSVM regression
dc.subject.otherOn-line prediction
dc.subject.otherTemporal series
dc.subject.otherReinforcement learning
dc.titleOn-line support vector machines for function approximation
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.rights.accessOpen Access
local.identifier.drac1890732
dc.description.versionPostprint (published version)
local.citation.authorMartin, M.


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