支持向量机中引入后验概率的理论和方法研究

STUDY OF THEORY AND METHOD INTRODUCING POSTERIORI PROBABILITY INTO SUPPORT VECTOR MACHINES

目前支持向量机解决模式识别问题是广大学者研究的热点，样本的后验概率在模式识别中至关重要，但是传统的支持向量机技术不提供后验概率．针对这一问题进行了３个方面的研究：①在给出样本点后验概率的基础上，将大规模优化问题分解成最大似然函数和最大分类边界两个小规模优化问题；②给出了一种新的用后验概率修正最优分离超平面的方法，并且分析了该新方法的合理性；③用图像分类的３组实例说明本方法的有效性．

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.