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IJON
2008

Support vector machine classification for large data sets via minimum enclosing ball clustering

8 years 7 months ago
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers. r 2007 Elsevier B.V. ...
Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where IJON
Authors Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li
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