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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The way of fault Diagnoses based on Support Vector Machine has a simple model compared with the traditional method. It also has great ability to classify, and the best generalization.

与传统的故障诊断方法相比，基于支持向量机的故障诊断方法具有模型简单、分类能力强、推广能力好等特点。

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Then, support vector machine is selected to classify aiming at clinical ECG data. Finally, classifications combination approach is analyzed.

随后针对实际的临床十二导联心电图数据，实验了两种支持向量机的分类方法。

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