Probability output modeling for support vector machines

Xiang Zhang; Xiaoling Xiao; Jinwen Tian; Jian Liu

doi:10.1117/12.742556

15 November 2007 Probability output modeling for support vector machines

Xiang Zhang, Xiaoling Xiao, Jinwen Tian, Jian Liu

Author Affiliations +

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880A (2007) https://doi.org/10.1117/12.742556
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Xiang Zhang, Xiaoling Xiao, Jinwen Tian, and Jian Liu "Probability output modeling for support vector machines", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880A (15 November 2007); https://doi.org/10.1117/12.742556

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