Dual margin approach on a Lagrangian support vector machine

@article{Hwang2011DualMA,
  title={Dual margin approach on a Lagrangian support vector machine},
  author={Jae Pil Hwang and Seongkeun Park and Euntai Kim},
  journal={Int. J. Comput. Math.},
  year={2011},
  volume={88},
  pages={695-708}
}
In this paper, we propose a new support vector machine (SVM) called dual margin Lagrangian support vectors machine (DMLSVM). Unlike other SVMs which use only support vectors to determine the separating hyperplanes, DMLSVM utilizes all the available training data for training the classifier, thus producing robust performance. The training data are weighted differently depending on whether they are in a marginal region or surplus region. For fast training, DMLSVM borrows its training algorithm… CONTINUE READING

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