Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model

  title={Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model},
  author={Hyunsoo Kim and Haesun Park},
A majordrawbackof supportvectormachinesis that the computational complexity for finding anoptimalsolution scalesas O(n), where n is the number of training data points. In this paper , we introducea novel ionic interaction model for data reduction in support vector machines.It is appliedto selectdatapointsandexclude outliers in the kernel featurespaceand producea data reduction algorithm with computationalcomplexity of aboutn/4 floatingpoint operations.Theinstance-based… CONTINUE READING
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Kerneldiscriminantanalysisbased on thegeneralizedsingularvaluedecomposition.Technical Report03-017,Departmentof ComputerScienceand Engineering,Universityof Minnesota,2003

  • C. Park andH. Park
  • 2003
1 Excerpt


  • H. Kim
  • Howland,andH. Park. Text classificationusing…
  • 2003
1 Excerpt

Predictionof proteinrelative solvent accessibilitywith supportvectormachinesandlong-range interaction3D local descriptor

  • H. Kim andH. Park
  • 2003
1 Excerpt

Proteinsecondarystructureprediction basedon an improved supportvector machinesapproach,2003.ProteinEng

  • H. Kim andH. Park
  • 2003
1 Excerpt

RSVM : Reduced supportvectormachines

  • O. L. Mangasarian.
  • 2003

Visualizing Mercer kernel featurespaces via kernelizedlocally-linearembeddings.In The8th InternationalConferenceon Neural InformationProcessing

  • D. DeCoste
  • 2001
1 Excerpt

Dataselectionfor support vectormachineclassification

  • IBM T. J. WatsonResearchCenter
  • ProceedingsKDD
  • 2000

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