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
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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.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Publication Date
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ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.10How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Pei-Yi Hao
PY  - NaN/NaN
DA  - NaN/NaN
TI  - Pruning Support Vectors in the SVM Framework and Its Application to Face Detection
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.10
DO  - https://doi.org/10.2991/jcis.2006.10
ID  - HaoNaN/NaN
ER  -