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Related Experiment Videos

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

Summary

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.

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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.
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.