• 支持向量基于支持向量机和方法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).

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  • 文中引入支持向量聚类SVC算法多分量LFM信号进行检测参数估计

    The support vector clustering(SVC)algorithm was introduced to detect linear modulation frequency(LFM) signal and estimate its parameter.

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  • 根据粗糙理论边界区域V -支持向量优点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.

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  • 提出一种基于聚类算法层次支持向量人脸识别方法

    In this paper a method of face recognition based on clustering algorithm and hierarchical support vector machines is presented.

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  • 为了解决支持向量应用于较小样本问题提出一种密度聚类支持向量机相结合的分算法

    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.

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  • 方法首先特征空间支持向量进行然后寻找特征空间中的聚类中心在输入空间中的原像形成约简向量

    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.

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  • 为了提高模糊支持向量在数据集上训练效率提出种改进的基于密度聚类(DBSCAN)的模糊支持向量算法

    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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  • 建模方法通过聚类方法输入空间划分些小的局部空间,每个局部空间中用最小支持向量机(LS -SVM)建立模型。

    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.

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  • 运用谱系聚类方法解决多核最小二乘支持向量缺乏稀疏问题

    The hierarchical clustering method is applied to deal with the problem that the solution of MLS-SVM is lack of sparseness.

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  • 方法基于模糊聚类算法挖掘不同之间衔接和离散信息设计树型支持向量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.

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  • KDDCUP 1999数据集上进行实验结果表明,与聚类支持向量方法相比方法能简化训练样本提高SVM训练检测速度

    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.

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  • 方法利用大量标识数据进行有效聚类结果用于样本情形下支持向量

    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.

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  • 方法利用大量标识数据进行有效聚类结果用于样本情形下支持向量

    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.

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