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dc.contributorPujol Vila, Oriol
dc.contributor.authorOrihuela Salvatierra, Helena
dc.date.accessioned2014-01-23T08:00:57Z
dc.date.available2014-01-23T08:00:57Z
dc.date.issued2013-09-14
dc.identifier.urihttp://hdl.handle.net/2099.1/20440
dc.description.abstractIn this thesis, background theory about the online kernel-based algorithms and their use for online learning is presented. The analysis of the state-ofthe- art methods highlights an important drawback in many kernel online learning algorithms. This is the large memory storage needed due to the amount of support vectors generated. We study the SCA approach for reducing support vectors in the batch learning case and propose its adaptation to the online scenario. POLSCA is the algorithm proposed for solving the addressed problems that online learning presents. The proposed algorithm is constructed by merging the concepts of Primal formulation of the optimization problem, online learning with stochastic subgradient descent solver(PEGASOS) and the support vector reduction method SCA.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshKernel functions
dc.subject.lcshMachine learning
dc.titleImproving sparsity in online kernel models
dc.typeMaster thesis
dc.subject.lemacKernel, Funcions de
dc.subject.lemacAprenentatge automàtic
dc.rights.accessOpen Access
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)


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