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dc.contributor.authorBarrio Moliner, Ignacio
dc.contributor.authorRomero Merino, Enrique
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-04-14T08:24:13Z
dc.date.available2020-04-14T08:24:13Z
dc.date.issued2007
dc.identifier.citationBarrio, I.; Romero, E.; Belanche, L. Selection of basis functions guided by the L2 soft margin. A: International Conference on Artificial Neural Networks. "Artificial Neural Networks, ICANN 2007, 17th International Conference: Porto, Portugal, September 9-13, 2007: proceedings, part I". Berlín: Springer, 2007, p. 421-430.
dc.identifier.isbn978-3-540-74690-4
dc.identifier.urihttp://hdl.handle.net/2117/183259
dc.description.abstractSupport Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, the number of support vectors can be large and, secondly, the model requires the use of (Mercer) kernel functions. Recently, some works have proposed to maximize the margin while controlling the sparsity. These works also require the use of kernels. We propose a search process to select a subset of basis functions that maximize the margin without the requirement of being kernel functions. The sparsity of the model can be explicitly controlled. Experimental results show that accuracy close to SVMs can be achieved with much higher sparsity. Further, given the same level of sparsity, more powerful search strategies tend to obtain better generalization rates than simpler ones.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshKernel functions
dc.subject.lcshSupport vector machines
dc.subject.otherBasis function
dc.subject.otherForward selection
dc.subject.otherRelevance vector machine
dc.subject.otherRadial basis function
dc.subject.otherSparse model
dc.titleSelection of basis functions guided by the L2 soft margin
dc.typeConference report
dc.subject.lemacKernel, Funcions de
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.1007/978-3-540-74690-4_43
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-540-74690-4_43
dc.rights.accessOpen Access
local.identifier.drac27666827
dc.description.versionPostprint (author's final draft)
local.citation.authorBarrio, I.; Romero, E.; Belanche, Ll.
local.citation.contributorInternational Conference on Artificial Neural Networks
local.citation.pubplaceBerlín
local.citation.publicationNameArtificial Neural Networks, ICANN 2007, 17th International Conference: Porto, Portugal, September 9-13, 2007: proceedings, part I
local.citation.startingPage421
local.citation.endingPage430


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