Weighted proximal support vector machines: Robust classification

@article{Meng2005WeightedPS,
  title={Weighted proximal support vector machines: Robust classification},
  author={Z. Meng and Fu Li-hua and Wang Gao-feng and Hu Ji-cheng},
  journal={Wuhan University Journal of Natural Sciences},
  year={2005},
  volume={10},
  pages={507-510}
}
Despite of its great efficiency for pattern classification, proximal support vector machines (PSVM), a new version of SVM proposed recently, is sensitive to noise and outliers. To overcome the drawback, this paper modifies PSVM by associating a weight value with each input data of PSVM. The distance between each data point and the center of corresponding class is used to calculate the weight value. In this way, the effect of noise is reduced. The experiments indicate that new SVM, weighted… CONTINUE READING
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