Abstract: Support vector machine is a new machine learning algorithm employing the criteria of structural risk minimization,which minimizes the errors between sample-data and model-data and decreases simultaneously the upper bound of predict error of model,SVM’s generalization is better than others. The characteristics of SVM ,such as the strong learing capability based on small samples, the good characteristic of generalization and insensitivity to random noise disturbance, are shown by its applications to the tabacum sensory evalution.

Key words: support vector machine , tabacum sensory, evaluation

摘要： 支持向量机是一种新的机器学习算法，它采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型预测误差的上界，从而提高了模型的泛化能力。本文通过SVM在烟叶感官品质评价中的应用，研究了SVM的小样本学习，泛化能力和抗噪声扰动能力。

关键词: 支持向量机, 烟叶感官品质, 评价