将其应用于模糊支持向量机方法中，较好地将支持向量与含噪声或野值样本区分开。
The fuzzy membership based on the affinity among samples for support vector machine effectively distinguishes between support vectors and outliers or noises.
仿真试验结果表明这种新的模糊支持向量机方法不但有较高的分类准确率，而且对隶属度有很强的预测能力。
Emulational experimental result shows that this new fuzzy support vector machine method not only has higher classified accuracy, but also has stronger test capability for the membership degree.
提出支持向量机-模糊预测控制方法，介绍支持向量机在列车启动控制过程中的应用。
It is proposed a fuzzy forecast control method based on support vector machine. The applications of the machine to the train start-up control are given.
该方法基于半模糊核聚类算法挖掘不同类别之间的衔接和离散信息，设计树型支持向量机的树型结构，克服其差错积累问题。
The method mines information on overlap between classes, designs the tree structure and overcomes the misclassification of tree-structured SVMs based on the semi-fuzzy kernel clustering algorithm.
应用模糊理论的方法对支持向量机分类及最优分类面进行了解释，对可疑分类区列出了模糊隶属度的表达式。
A method based on fuzzy theory is applied to explain the classification of SVM and its optimal hyperplane. An expression of fuzzy membership on doubtful classification area is listed.
应用模糊理论的方法对支持向量机分类及最优分类面进行了解释，对可疑分类区列出了模糊隶属度的表达式。
A method based on fuzzy theory is applied to explain the classification of SVM and its optimal hyperplane. An expression of fuzzy membership on doubtful classification area is listed.
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