A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

@article{Guo2008ANL,
  title={A novel LS-SVMs hyper-parameter selection based on particle swarm optimization},
  author={X. C. Guo and J. H. Yang and C. G. Wu and C. Y. Wang and Y. C. Liang},
  journal={Neurocomputing},
  year={2008},
  volume={71},
  pages={3211-3215}
}
The selection of hyper-parameters plays an important role to the performance of least-squares support vector machines (LS-SVMs). In this paper, a novel hyper-parameter selection method for LS-SVMs is presented based on the particle swarm optimization (PSO). The proposed method does not need any priori knowledge on the analytic property of the generalization performance measure and can be used to determine multiple hyper-parameters at the same time. The feasibility of this method is examined on… CONTINUE READING
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Quantitative structure– activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines

  • J. H. Li, H. X. Liu, X. J. Yao, M. C. Liu, Z. D. Hu, B. T. Fan
  • Chemometrics Intell. Lab. Syst. 87
  • 2007
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