通过将类不变性先验信息融入到支持向量机(Support Vector Machine, SVM)算法的目标函数中，提出了一种基于漂移约束的SVM红外光谱定性分析算法。该算法将红外光谱的漂移项模拟 成一个低阶多项式，并在SVM优化目标中要求决策面的法向量与漂移方向垂直，从而使分类器能够消除样本漂移影响。详细讨论了波段选择和正则化参数对分 类准确率的影响，并对比了各种变形SVM算法的分类效果。实验结果表明，与标准的SVM算法及其各种变形算法相比，本文提出的DCSVM算法具有更高的分类准确度。

By incorporating class-invariant prior information into the object function of a Support Vector Machine (SVM) algorithm, a SVM infrared spectroscopic qualitative analysis algorithm based on drift constraint is proposed. Because the algorithm simulates the drift term of infrared spectrum into a low order polynomial and requires the normal vector of the decision surface to be perpendicular to the drift direction, the classifier can remove the effect of sample drift. The influence of band selection and regularization parameters on classification accuracy is described in detail and the classification results of different SVM algorithms are compared. The experimental result shows that compared with the standard SVM algorithm and other similar algorithms, the DCSVM algorithm has a higher classification accuracy.