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

  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.},
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


Publications citing this paper.
Showing 1-6 of 6 extracted citations

A Novel Classifier-Independent Feature Selection Algorithm for Imbalanced Datasets

2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing • 2009
View 4 Excerpts
Highly Influenced

Study of dependency between the input noise and the parameter in fuzzy linear regression model

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) • 2011
View 3 Excerpts

Air Mass Factor Calculations Based on Support Vector Regression Machine

2009 Second International Conference on Environmental and Computer Science • 2009


Publications referenced by this paper.
Showing 1-10 of 11 references

Handbook of practical mathematics, Chinese Science

S Yonghuan
View 1 Excerpt

Artificial neural networks and evolutionary computation

Yan Pinfan
View 2 Excerpts

An Introduction to Support Vector Machines.Cambridge

N Cristianini, J Shawe-Taylor
View 1 Excerpt

Similar Papers

Loading similar papers…