Communications for Statistical Applications and Methods

  • 제19권5호
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  • Pages.655-662
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  • 2012
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Multiclass Support Vector Machines with SCAD

  • Jung, Kang-Mo (Department of Statistics and Computer Science, Kunsan National University)
  • 투고 : 2012.06.26
  • 심사 : 2012.07.17
  • 발행 : 2012.09.30
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초록

Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation(SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the $L_1$, $L_2$ penalty functions and the developed method.

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참고문헌

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피인용 문헌

  1. Support Vector Machines for Unbalanced Multicategory Classification vol.2015, 2015, https://doi.org/10.1155/2015/294985
  2. Weighted Support Vector Machines with the SCAD Penalty vol.20, pp.6, 2013, https://doi.org/10.5351/CSAM.2013.20.6.481