Large-Scale Data Classification Based on Ball Vector Machine

Min Juan Zheng; Guo Jian Cheng; Fei Zhao

doi:10.4028/www.scientific.net/AMM.312.771

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 312Large-Scale Data Classification Based on Ball...

Large-Scale Data Classification Based on Ball Vector Machine

Abstract:

The quadratic programming problem in the standard support vector machine (SVM) algorithm has high time complexity and space complexity in solving the large-scale problems which becomes a bottleneck in the SVM applications. Ball Vector Machine (BVM) converts the quadratic programming problem of the traditional SVM into the minimum enclosed ball problem (MEB). It can indirectly get the solution of quadratic programming through solving the MEB problem which significantly reduces the time complexity and space complexity. The experiments show that when handling five large-scale and high-dimensional data sets, the BVM and standard SVM have a considerable accuracy, but the BVM has higher speed and less requirement space than standard SVM.

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Periodical:

Applied Mechanics and Materials (Volume 312)

Pages:

771-776

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.312.771

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Online since:

February 2013

Authors:

Min Juan Zheng, Guo Jian Cheng, Fei Zhao

Keywords:

Ball Vector Machine, Core Vector Machine, Enclosed Ball, Minimum Enclosed Ball, Support Vector Machine (SVM)

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References

[1] Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung. Core Vector Machines: Fast SVM Training on Very Large Data Sets. Journal of Machine Learning Research, 6: 363-392, 2005.

Google Scholar

[2] Ivor W. Tsang, Andras Kocsor, James T. Kwok. Simpler Core Vector Machines with Enclosing Balls. Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML), Corvallis, Oregon, USA, June 2007.

DOI: 10.1145/1273496.1273611

Google Scholar

[3] Liang Chan. The Geometric Relationship between Core Vector Machine and Support Vector Machine. World Congress on Intelligent and Automation. Chongqing, China. June 2008.

DOI: 10.1109/wcica.2008.4593638

Google Scholar

[4] UCI Repository of Machine Learning Database [DB/OL]. http://archive.ics.uci.edu/ml/index.html.

Google Scholar

[5] Pascal Large Scale Learning Challenge [DB/OL]. http://largescale.first.fraunhofer.de.

Google Scholar

[6] libSVM[CP/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

Google Scholar

[7] libCVM[CP/OL]. http://www.cse.ust.hk/~ivor/cvm.html.

Google Scholar

[8] Zuyou Lu, Yongshen Sang. Chinese Text Classification based on the Ball Vector Machine [J]. Computer Engineering and Science, 2008, 12: 82-84.

Google Scholar

[9] Guihua Wen, Jun Xiang, Yuehua Ding. Large-scale SVM Classification Algorithm based on Granularity of Quotient Space Theory [J]. Computer Application Research, .2008, 25(8): 2229-2301.

Google Scholar

[10] Junyi Pu, Xiuren Lei. Two-stage Fast Training Method based on Core Vector Machine and Support Vector Machine, 2012, 32 (2): 419-424.

DOI: 10.3724/sp.j.1087.2012.00419

Google Scholar