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Proceedings Paper

Probability output modeling for support vector machines
Author(s): Xiang Zhang; Xiaoling Xiao; Jinwen Tian; Jian Liu
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Paper Abstract

In this paper we propose an approach to model the posterior probability output of multi-class SVMs. The sigmoid function is used to estimate the posterior probability output in binary classification. This approach modeling the posterior probability output of multi-class SVMs is achieved by directly solving the equations that are based on the combination of the probability outputs of binary classifiers using the Bayes's rule. The differences and different weights among these two-class SVM classifiers, based on the posterior probability, are considered and given for the combination of the probability outputs among these two-class SVM classifiers in this method. The comparative experiment results show that our method achieves the better classification precision and the better probability distribution of the posterior probability than the pairwise couping method and the Hastie's optimization method.

Paper Details

Date Published: 15 November 2007
PDF: 5 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880A (15 November 2007); doi: 10.1117/12.742556
Show Author Affiliations
Xiang Zhang, Yangtze Univ. (China)
Key Lab. of Exploration Technologies for Oil and Gas Resources, Ministry of Education (China)
Xiaoling Xiao, Yangtze Univ. (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)
Jian Liu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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