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中文摘要：
针对现实环境中样本集越来越大，并且往往含有大量噪声和野值，导致传统模糊支持向量机的训练时间和分类识别率降低的阃题，提出基于密度法的双隶属度模糊支持向量机，即靠近类中心的样本点隶属度由其到类中心的距离确定，远离类中心的样本点隶属度由其邻域内同类异类样本点数量的比例确定。从理论和实证两个方面分析文中方法与以往基于密度的模糊支持向量机（DFSVM）相比，该方法不但降低了算法的复杂度，并且提高了支持向量机的分类精度。
英文摘要：
In this paper, an improved fuzzy membership function determination is proposed to train the fuzzy support vector machine （FSVM） for classification Mxich the sample set in reality environment is increasing, and it often contains a lot of noise and outliers. In the improved algorithm, the sample points have the different types of memberships in different regions. That is, the membership of the sampie point near by the class centers is detemined by the distance between the point and its class center, and the membership of the .sample point far away the class centers is determined by the proportion between the number of its congeneric points and the number of its heterogeneous points in its neighborhood. The dual membership is introduced to reduce the algorithm complexity and shorten its training time compared with fuzzy support vector machine based on density, at the same time the algorithm well improves the SVM＇s aceursey rate.
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