Training algorithms for fuzzy support vector machines with noisy data
Training algorithms for fuzzy support vector machines with noisy data
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DOI：
10.1109/nnsp.2003.1318051
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发表时间：
2003-09
期刊：
影响因子：
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通讯作者：
Chun-fu Lin;Sheng-de Wang
中科院分区：
文献类型：
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作者：
Chun-fu Lin;Sheng-de Wang
模糊支持向量机（FSVMs）提供了一种方法来分类数据与噪声或离群值。每个数据点都与一个模糊隶属度相关联，可以将它们的相对程度反映为有意义的数据。在本文中，我们研究和比较两种策略，自动设置数据点的模糊隶属度。这使得FSVM在减少噪声或野值影响的应用中更容易使用。实验表明，FSVMs的泛化误差与基准数据集上的其他方法相当。
Fuzzy support vector machines (FSVMs) provide a method to classify data with noises or outliers. Each data point is associated with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we investigate and compare two strategies of automatically setting the fuzzy memberships of data points. It makes the usage of FSVMs easier in the application of reducing the effects of noises or outliers. The experiments show that the generalization error of FSVMs is comparable to other methods on benchmark datasets.