Title of article
Kernel basedsupportvectormachineviasemidefiniteprogramming:Applicationto medical diagnosis
Author/Authors
Domenico Conforti، نويسنده , , RositaGuido، نويسنده ,
Issue Information
ماهنامه با شماره پیاپی سال 2010
Pages
6
From page
1389
To page
1394
Abstract
Support vectormachine(SVM)isawellsoundlearningmethodandarobustclassificationprocedure.
Choosing asuitablekernelfunctioninSVMiscrucialforobtaininggoodperformance;thedifficultyis
how tochooseasuitabledatatransformationforthegivenproblem.Tothisend,multiplekernelmatrices,
each ofthemcorrespondingtoagivensimilaritymeasure,canbelinearlycombined.Inthispaper,the
optimal kernelmatrix,obtainedaslinearcombinationofknownkernelmatrices,isgeneratedusinga
semidefinite programmingapproach.Asuitablemodelformulationassuresthattheobtainedkernelma-
trix ispositivesemidefiniteandisoptimalwithrespecttothedatasetunderconsideration.Theproposed
approach hasbeenappliedtosomeveryimportantmedicaldiagnosticdecisionmakingproblemsandthe
results obtainedbycarryingoutpreliminarynumericalexperimentsdemonstratedtheeffectivenessof
the proposedsolutionapproach.
Keywords
classification , support vector machines , Semidefinite programming , Medical diagnosis , Kernel function , Machine learning
Journal title
Computers and Operations Research
Serial Year
2010
Journal title
Computers and Operations Research
Record number
927748
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