An Efficient Method for Large Margin Parameter

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dc.contributor.author Yu, Huizhen en
dc.contributor.author Rousu, Juho en
dc.date.accessioned 2008-01-14T08:37:07Z en
dc.date.accessioned 2009-06-17T13:51:24Z
dc.date.available 2008-01-14T08:37:07Z en
dc.date.available 2009-06-17T13:51:24Z
dc.date.issued 2008-01-14T08:37:07Z en
dc.identifier.uri http://hdl.handle.net/10138/1140
dc.description.abstract We consider structured prediction problems with a parametrized linear prediction function, and the associated parameter optimization problems in large margin type of discriminative training. We propose a dual optimization approach which uses the restricted simplicial decomposition method to optimize a reparametrized dual problem. Our reparametrization reduces the dimension of the space of the dual function to one that is linear in the number of parameters and training examples, and hence independent of the dimensionality of the prediction outputs. This in conjunction with simplicial decomposition makes our approach efficient. We discuss the connections of our approach with related earlier works, and we show its advantages. en
dc.language.iso en en
dc.relation.ispartofseries Dept. of Computer Science Series of Publications C en
dc.relation.ispartofseries C-2007-87 en
dc.title An Efficient Method for Large Margin Parameter en
dc.type Technical Report en
dc.identifier.laitoskoodi 523 fi
dc.creator.corporateName Tietojenkäsittelytieteen laitos fi
dc.creator.corporateName Department of Computer Science en
dc.creator.corporateName Datavetenskap, Institutionen för sv

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