The key problem of training support vector machines is how to solve quadratic programming problem, but for large training examples, the problem is too difficult.

训练支持向量机的本质问题就是求解二次规划问题，但对大规模的训练样本来说，求解二次规划问题困难很大。

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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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Through support vector machine algorithms for gene expression data classification training, SVMs provide a effective way for analysis of gene expression data.

通过支持向量机训练算法对基因表达数据进行分类训练，为分析基因数据提供有效的手段。

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