M-estimator based robust kernels for support vector machines

@article{Chen2004MestimatorBR,
  title={M-estimator based robust kernels for support vector machines},
  author={Jiun-Hung Chen},
  journal={Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.},
  year={2004},
  volume={1},
  pages={168-171 Vol.1}
}
In this paper, we propose M-estimator based robust kernels for support vector machine. The main motivation for our proposed kernels is that the sum of squared difference in the widely used Gaussian radial basis function kernels is not robust to outlier or noise. In addition, inspired by using a robust loss function in support vector machine regression to control training error and the idea of robust template matching with M-estimator, we apply M-estimator techniques to Gaussian radial basis… CONTINUE READING
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