Classification using support vector machines with graded resolution

被引：0

作者：

Wang, LP ^{[1
]}

Liu, B ^{[1
]}

Wan, CR ^{[1
]}

机构：

[1] Xiangtan Univ, Coll Informat Engn, Xiangtan, Hunan, Peoples R China

来源：

2005 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2 | 2005年

关键词：

granular support vector machine; granular computing;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.

引用

页码：666 / 670

页数：5