本文介绍了SVM 支持向量机的分类技术，以中医心系503 个样本为例，利用SVM 进行中医心系证候分类研究，实验结果表明，该方法在证候分类中能达到较高的准确率。

The support vector machine(SVM) is a new kind of machine learning method. Based on the structural risk minimization rule, the SVM has good generalization ability. As the SVM algorithm has been proved to be a convex quadratic optimization problem, any extremal solution is definitely a global optimal solution. This paper introduces the SVM classification techniques, and analyzes 503 cases of heart diseases using the SVM. The results show that this method may help to realize syndromes classification at high precision.

科学技术部国家“十一五”科技支撑计划子项目（2006BAI08B01）：中医四诊信息规范采集和融合方法的研究，负责人：王忆勤；上海市科委优秀学科带头人计划项目（09XD1403700）：中医四诊信息融合方法研究，负责人：王忆勤。