An Efficient Method for Large Margin Parameter

Visa fullständig post



Permalänk

http://hdl.handle.net/10138/1140
Titel: An Efficient Method for Large Margin Parameter
Författare: Yu, Huizhen; Rousu, Juho
Datum: 2008-01-14
Språk: en
Tillhör serie: Dept. of Computer Science Series of Publications C - C-2007-87
Permanenta länken (URI): http://hdl.handle.net/10138/1140
Abstrakt: 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.


Filer under denna titel

Totalt antal nerladdningar: Laddar...

Filer Storlek Format Granska
StrPred-YR.pdf 244.2Kb PDF Granska/Öppna

Detta dokument registreras i samling:

Visa fullständig post