根据粗糙集理论的边界区域和V -支持向量机的优点对支持向量聚类算法进行改进。
According to the border region of rough set theory and the merits of V-support vector machine, the algorithm of support vector clustering is improved.
文中引入支持向量聚类（SVC）算法对多分量LFM信号进行检测和参数估计。
The support vector clustering(SVC)algorithm was introduced to detect linear modulation frequency(LFM) signal and estimate its parameter.
提出一种基于聚类算法和层次支持向量机的人脸识别方法。
In this paper a method of face recognition based on clustering algorithm and hierarchical support vector machines is presented.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
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.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
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