支持向量聚类是基于支持向量机和核方法的一种新颖的聚类方法。
Rough set was applied to clustering method in view of soft kernel of support vector clustering(SVC).
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.
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.
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.
此方法首先在特征空间中对支持向量进行聚类，然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集。
The method firstly organizes support vectors in clusters in feature space, and then, it finds the pre-images of the cluster centroids in feature spa.
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.
该建模方法通过减聚类方法将输入空间划分为一些小的局部空间，在每个局部空间中用最小二乘支持向量机(LS -SVM)建立子模型。
In this method, subtractive clustering was adopted to divide the input space into several sub-spaces, and sub-models were built by Least Square SVM (ls SVM) in every sub-space.
运用谱系聚类方法解决多核最小二乘支持向量机的解缺乏稀疏性的问题。
The hierarchical clustering method is applied to deal with the problem that the solution of MLS-SVM is lack of sparseness.
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.
在KDDCUP 1999数据集上进行实验，结果表明，与聚类支持向量机方法相比，该方法能简化训练样本，提高SVM的训练和检测速度。
Experimental results on KDDCUP1999 data-set show that the method is more effective than cluster SVM in reducing training samples and improving the training and detecting speed of SVM.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
应用推荐