An Approach to Choosing Gaussian Kernel Parameter for One-Class SVMs via Tightness Detecting

@article{Wang2012AnAT,
  title={An Approach to Choosing Gaussian Kernel Parameter for One-Class SVMs via Tightness Detecting},
  author={Huangang Wang and Lin Zhang and Yingchao Xiao and Wenli Xu},
  journal={2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics},
  year={2012},
  volume={2},
  pages={318-323}
}
In recent years, one-class support vector machines (OCSVMs) have received increasing attention, which are one of the methods to solve one-class classification problems. Among all the kernels available to OCSVMs, Gaussian kernel is the most commonly used one with a single parameter S to tune, which influences classifier performance significantly. This paper proposes a novel heuristic approach to choosing this parameter via tightness detecting, that is designed to detect whether the decision… CONTINUE READING

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