9th Joint International Conference on Information Sciences (JCIS-06)

Pruning Support Vectors in the SVM Framework and Its Application to Face Detection

Authors
Pei-Yi Hao 0
Corresponding author
Pei-Yi Hao
0National Kaohsiung University of Applied Sciences
DOI
https://doi.org/10.2991/jcis.2006.10How to use a DOI?
Keywords
support vector machine, network pruning, model selection, kernel-based learning, face detection.
Abstract
This paper presents the pruning algorithms to the support vector machine for sample classification and function regression. When constructing support vector machine network we occasionally obtain redundant support vectors which do not significantly affect the final classification and function approximation results. The pruning algorithms primarily based on the sensitivity measure and the penalty term. The kernel function parameters and the position of each support vector are updated in order to have minimal increase in error, and this makes the structure of SVM network more flexible. We illustrate this approach with synthetic data simulation and face detection problem in order to demonstrate the pruning effectiveness.
Copyright
© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
Open Access | Under Creative Commons license CC BY-NC 4.0

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@inproceedings{Hao2006,
  title={Pruning Support Vectors in the SVM Framework and Its Application to Face Detection},
  author={Hao, Pei-Yi},
  year={2006},
  booktitle={9th Joint International Conference on Information Sciences (JCIS-06)},
  issn={1951-6851},
  isbn={978-90-78677-01-7},
  url={http://dx.doi.org/10.2991/jcis.2006.10},
  doi={10.2991/jcis.2006.10},
  publisher={Atlantis Press}
}
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