Automated Parameter Selection for Support Vector Machine Decision Tree

  • Gyunghyun Choi
  • Suk Joo Bae
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.


Support Vector Machine Decision Node Nonlinear Pattern Automate Scheme Census Income 
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 2006

Authors and Affiliations

  • Gyunghyun Choi
    • 1
  • Suk Joo Bae
    • 1
  1. 1.Department of Industrial EngineeringHanyang UniversitySeoulKorea

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