Unified Kernel Function and Its Training Method for SVM

  • Ha-Nam Nguyen
  • Syng-Yup Ohn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


This paper proposes a unified kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the unified kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a new learning method based on genetic algorithm. The weights of basis kernel functions in the unified kernel are determined in learning phase and used as the parameters in the decision model in the classification phase. The unified kernel and the learning method were applied to obtain the optimal decision model for the classification of two public data sets for diagnosis of cancer diseases. The experiment showed fast convergence in learning phase and resulted in the optimal decision model with the better performance than other kernels. Therefore, the proposed kernel function has the greater flexibility in representing a problem space than other kernel functions.


Support Vector Machine Kernel Function Learning Method Learning Phase High Dimensional Feature Space 
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

  • Ha-Nam Nguyen
    • 1
  • Syng-Yup Ohn
    • 1
  1. 1.Department of Computer EngineeringHankuk Aviation UniversitySeoulKorea

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