Weighted posterior probability output for support vector machines
Guangyou Xu · Journal of Tsinghua University(Science and Technology) · 2007
A weighted posterior probability method is presented to calculate the probability outputs of support vector machines(SVMs) for multi-class cases.The differences and weights for combination of the probabilty output among these two-class classifiers calculated from the posterior probability are given based on the Bayesian theory.Tests show that the weighted posterior probability method has less classification errors,better classification ability,and a better probability distribution of the posterior probability than the voting method or the Pairwise Coupling method.This method effectively provides probability outputs of SVMs in the multi-class case.