Abstract: The technology of support vector machines is being used to solve problems of pattern recognition. Posteriori probability of samples is important in pattern recognition. But standard support vector machines do not provide posteriori probability. Discussed below are several questions based upon posteriori probability in the support vector machine: （1） decomposing the nonlinear optimal problem of a large training sample set into two nonlinear optimal problems of small training set; （2） designing the algorithm to revise the traditional optimal hyperplane, and analyzing the rationality of the algorithm; and （3） showing the results from testing on three image data sets effectively.