Rough set was applied to clustering method in view of soft kernel of support vector clustering(SVC).
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。
The support vector clustering(SVC)algorithm was introduced to detect linear modulation frequency(LFM) signal and estimate its parameter.
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。
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.
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。
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。
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.
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。
In this paper a method of face recognition based on clustering algorithm and hierarchical support vector machines is presented.
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。
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 paper's main works is that: learning algorithm studies of support vector machine, mathematical model and application about feature selection, convergence analysis of clustering algorithm.
本文主要致力于支持向量机、近似支持向量机的学习算法研究，特征提取的数学模型与算法的改进及其应用，聚类分析算法的收敛性证明。
This paper's main works is that: learning algorithm studies of support vector machine, mathematical model and application about feature selection, convergence analysis of clustering algorithm.
本文主要致力于支持向量机、近似支持向量机的学习算法研究，特征提取的数学模型与算法的改进及其应用，聚类分析算法的收敛性证明。
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