Method for determining parameters of posterior probability SVM based on relative cross entropy

Authors:
Qing-hua Xing;Fu-xian Liu;Xiang Li;Lu Xia
Affiliations:
Missile Institute of Air Force Engineering University, Sanyuan, China;Missile Institute of Air Force Engineering University, Sanyuan, China;Missile Institute of Air Force Engineering University, Sanyuan, China;Missile Institute of Air Force Engineering University, Sanyuan, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

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Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV

Advances in kernel methods

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Abstract

The technology of support vector machines (SVM) is being widely used in many research fields at present, but standard SVM does not provide posterior probability that is needed in many uncertain classification problems. To solve this problem, a probability SVM model is built firstly, then the cross entropy and relative cross entropy model for classification problems are built. Finally, the method for determining parameters of probability SVM model is put forward by minimizing relative cross entropy. Experiment results show that the method of determining model parameters is reasonable, and the posterior probability SVM model is effective.