Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Asymmetric least squares regression estimation using weighted least squares support vector machine

  • Received : 2011.08.31
  • Accepted : 2011.09.21
  • Published : 2011.10.01
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Abstract

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

Keywords

References

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