On the use of the Spectral Projected Gradient method for Support Vector Machines

creativework.keywordsSupport Vector Machines, Quadratic Programming, Spectral Projected Gradient method.
dc.contributor.authorCores, Débora
dc.contributor.authorEscalante, René
dc.contributor.authorGonzález-Lima, María
dc.contributor.authorJiménez, Oswaldo
dc.date.accessioned2025-01-28T10:02:50Z
dc.date.available2025-01-28T10:02:50Z
dc.date.issued2009
dc.description.abstractIn this work we study how to solve the SVM optimization problem by using the Spectral Projected Gradient (SPG) method with three different strategies for computing the projection onto the constrained set. One of the strategies is based on Dykstra’s alternating projection algorithm since there is not a mathematical equation for the projection onto the whole constrained set but the projection on each restriction is easy to compute with exact formulations. We present another strategy based on the Karush-Kunh-Tucker optimality conditions, we call it the Projected-KKT algorithm. We compare these strategies with a third one proposed by Dai and Fletcher. The three schemes are low computational cost and their use within the SPG algorithm leads to a solution of the SVM problem. We study the computational performance of the three strategies when solving randomly generated as well as real life SVM problems. The numerical results show that Projected-KKT is competitive in general with the Dai and Fletcher algorithm, and it is more efficient for some specific problems. They both outperform Dykstra’s algorithm in all the tests.
dc.identifier.citationComputational and Applied Mathematics, 28(3), 1-38, 2009
dc.identifier.issn2244-8659
dc.identifier.urihttp://calderon.cud.uvigo.es/handle/123456789/886
dc.language.isoen
dc.publisherComputational and Applied Mathematics
dc.titleOn the use of the Spectral Projected Gradient method for Support Vector Machines
dc.typeArticle
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