Theoretically Optimal Parameter Choices for Support Vector Regression Machines with Noisy Input

@article{Wang2005TheoreticallyOP,
  title={Theoretically Optimal Parameter Choices for Support Vector Regression Machines with Noisy Input},
  author={Shitong Wang and Jiagang Zhu and Korris Fu-Lai Chung and Lin Qing and Dewen Hu},
  journal={Soft Comput.},
  year={2005},
  volume={9},
  pages={732-741}
}
With the evidence framework, the regularized linear regression model can be explained as the corresponding MAP problem in this paper, and the general dependency relationships that the optimal parameters in this model with noisy input should follow is then derived. The support vector regression machines Huber-SVR and Norm-r r-SVR are two typical examples of thismodel and their optimal parameter choices are paid particular attention. It turns out that with the existence of the typical Gaussian… CONTINUE READING

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