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International journal of fuzzy logic and intelligent systems : IJFIS v.9 no.2, 2009년, pp.141 - 146   피인용횟수: 2
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Support Vector Machine based on Stratified Sampling

Jun, Sung-Hae    (Department of Bioinformatics & Statistics, Cheongju University  );
  • 초록

    Support vector machine is a classification algorithm based on statistical learning theory. It has shown many results with good performances in the data mining fields. But there are some problems in the algorithm. One of the problems is its heavy computing cost. So we have been difficult to use the support vector machine in the dynamic and online systems. To overcome this problem we propose to use stratified sampling of statistical sampling theory. The usage of stratified sampling supports to reduce the size of training data. In our paper, though the size of data is small, the performance accuracy is maintained. We verify our improved performance by experimental results using data sets from UCI machine learning repository.


  • 주제어

    Support Vector Machine .   Stratified Sampling .   Classification .   Computing Cost.  

  • 참고문헌 (28)

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  • 이 논문을 인용한 문헌 (2)

    1. Jun, Sung-Hae 2011. "A New Statistical Sampling Method for Reducing Computing time of Machine Learning Algorithms" 한국지능시스템학회 논문지 = Journal of Korean institute of intelligent systems, 21(2): 171~177     
    2. 2012. "" International journal of fuzzy logic and intelligent systems : IJFIS, 12(1): 6~14     

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