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

Author:

Cervantes Jair,Li Xiaoou,Yu Wen,Li Kang

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference26 articles.

1. M. Awad, L. Khan, F.Bastani, I. L.Yen, An effective support vector machine SVMs performance using hierarchical clustering, in: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’04), 2004, pp. 663–667.

2. M. Badoiu, S. Har-Peled, P. Indyk. Approximate clustering via core-sets, in: Proceedings of the 34th Symposium on Theory of Computing, 2002.

3. A comparative study of ordinary cross-validation, v-Fold cross-validation and the repeated learning-testing methods;Burman;Biometrika,1989

4. J. Cervantes, X. Li, W. Yu, Support vector machine classification based on fuzzy clustering for large data sets, in: MICAI 2006: Advances in Artificial Intelligence, Lecture Notes in Computer Science (LNCS), vol. 4293, Springer, Berlin, 2006, pp. 572–582.

5. C.-C.Chang, C.-J. Lin, LIBSVM: a library for support vector machines, 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉, 2001.

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