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Radius margin bounds for support vector machines with the RBF kernel.

Kai-Min Chung1, Wei-Chun Kao, Chia-Liang Sun

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b88061@csie.ntu.edu.tw

Neural Computation
|October 28, 2003
PubMed
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This study enhances support vector machine (SVM) model selection by proposing modified radius margin bounds for L1-SVM, achieving performance comparable to L2-SVM. The research highlights that bound minima location is key for efficient model selection.

Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Efficient Support Vector Machine (SVM) model selection is crucial.
  • Differentiable bounds of leave-one-out (LOO) error are a key approach.
  • Existing bounds like radius margin and span bounds have limitations in practical viability.

Purpose of the Study:

  • To analyze why radius margin bounds perform well for L2-SVM.
  • To propose modified radius margin bounds for L1-SVM, improving upon existing methods.
  • To compare L1-SVM and L2-SVM based on properties like differentiability and support vector count.

Main Methods:

  • Analysis of radius margin bounds for L2-SVM.
  • Development of modified radius margin bounds for L1-SVM.
  • Comparative analysis of L1-SVM and L2-SVM characteristics.

Related Experiment Videos

Main Results:

  • The location of bound minima is more critical than tightness for small LOO values.
  • Modified radius margin bounds for L1-SVM demonstrate performance comparable to L2-SVM.
  • L1-SVM offers an advantage with fewer support vectors compared to L2-SVM.

Conclusions:

  • Modified radius margin bounds offer an effective approach for L1-SVM model selection.
  • L1-SVM presents advantages in terms of support vector efficiency.
  • Understanding bound properties is essential for optimizing SVM model selection.