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。

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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。

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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。

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