Parallel Tuning of Support Vector Machine Learning Parameters for Large and Unbalanced Data Sets

  • Tatjana Eitrich
  • Bruno Lang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3695)


We consider the problem of selecting and tuning learning parameters of support vector machines, especially for the classification of large and unbalanced data sets. We show why and how simple models with few parameters should be refined and propose an automated approach for tuning the increased number of parameters in the extended model. Based on a sensitive quality measure we analyze correlations between the number of parameters, the learning cost and the performance of the trained SVM in classifying independent test data. In addition we study the influence of the quality measure on the classification performance and compare the behavior of serial and asynchronous parallel parameter tuning on an IBM p690 cluster.


Support Vector Machine Quality Measure Trial Point Training Support Vector Machine Fuzzy Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tatjana Eitrich
    • 1
  • Bruno Lang
    • 2
  1. 1.John von Neumann Institute for Computing, Central Institute for Applied MathematicsResearch Centre JuelichGermany
  2. 2.Applied Computer Science and Scientific Computing Group, Department of MathematicsUniversity of WuppertalGermany

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