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Robust support vector regression networks for function approximation with outliers.

Chen-Chia Chuang1, Shun-Feng Su, Jin-Tsong Jeng

  • 1Dept. of Electron. Eng., Hwa-Hsia Coll. of Technol. and Commerce, Taipei, Taiwan.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
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This study introduces a robust support vector regression (RSVR) network to improve support vector regression (SVR) performance. RSVR effectively suppresses overfitting and enhances model robustness, even with improper parameter selection.

Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Support vector regression (SVR) is used for function approximation and regression.
  • SVR exhibits robustness to noise but can overfit due to improper parameter selection or outliers.

Purpose of the Study:

  • To propose a novel robust support vector regression (RSVR) network.
  • To enhance the robust capabilities and learning performance of SVR.

Main Methods:

  • The proposed RSVR network integrates traditional robust learning approaches.
  • This method aims to improve learning performance irrespective of parameter selection.

Main Results:

  • Simulation results demonstrate that RSVR consistently improves learned system performance.

Related Experiment Videos

  • RSVR effectively suppresses overfitting, as testing errors do not increase with prolonged training.

    • Conclusions:

    • The novel RSVR network enhances SVR's robustness and learning performance.
    • RSVR successfully mitigates overfitting issues inherent in traditional SVR.