Abstract
As a powerful tool in machine learning, support vector machine (SVM) suffers from expensive computational cost in the training phase due to the large number of original training samples. In addition, Minimal Enclosing Ball (MEB) has a limitation with a large dataset, and the training computational increases as data size becomes large. This paper presents an improved two approaches based SVMs reduced to Minimal Enclosing Ball (MEB) problems. These approaches find the concentric balls with minimum volume of data description to reduce the chance of accepting abnormal data that contain most of the training samples. Our study is experimented on speech information to eliminate all noise data and reducing time training. Numerical experiments on some real-world datasets verify the usefulness of our approaches for data mining.
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Nour-Eddine, L., Abdelkader, A. (2014). Reduced Data Based Improved MEB/L2-SVM Equivalence. In: MartÃnez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-RodrÃguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_1
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DOI: https://doi.org/10.1007/978-3-319-07491-7_1
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