Training algorithms for fuzzy support vector machines with noisy data

被引：15

作者：

Lin, CF ^{[1
]}

Wang, SD ^{[1
]}

机构：

[1] Natl Taiwan Univ, Dept Elect Engn, Taipei 106, Taiwan

来源：

2003 IEEE XIII WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING - NNSP'03 | 2003年

关键词：

D O I：

10.1109/NNSP.2003.1318051

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：517 / 526

页数：10

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