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Volume 33 Issue 5
Jun.  2011
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Tao Jian-Wen, Wang Shi-Tong. Maximal Margin Support Vector Machine with Magnetic Field Effect[J]. Journal of Electronics & Information Technology, 2011, 33(5): 1055-1061. doi: 10.3724/SP.J.1146.2010.00896
Citation: Tao Jian-Wen, Wang Shi-Tong. Maximal Margin Support Vector Machine with Magnetic Field Effect[J]. Journal of Electronics & Information Technology, 2011, 33(5): 1055-1061. doi: 10.3724/SP.J.1146.2010.00896

Maximal Margin Support Vector Machine with Magnetic Field Effect

doi: 10.3724/SP.J.1146.2010.00896 cstr: 32379.14.SP.J.1146.2010.00896
  • Received Date: 2010-08-24
  • Rev Recd Date: 2010-12-10
  • Publish Date: 2011-05-19
  • In this paper, a novel maximal margin Support Vector Machine with Magnetic Field effect (MFSVM) is proposed in allusion to the improvement of the generalization performance of pattern classification issue. By introducing a minimum q-magnetic field tape, the basic idea of MFSVM is to find an optimal hyper-plane with magnetic field effect such that one class (or normal patterns) can be enclosed in the q-magnetic field tape due to the magnetic attractive effect, while at the same time the margin between the q-magnetic field tape and the other class (or abnormal patterns) is as large as possible due to magnetic repulsion, thus implementing both maximum between-class margin and minimum within-class volume so as to improve the generalization capability of the proposed method. Experimental results obtained with benchmarking and synthetic datasets show that the proposed algorithm is effective and competitive to other related methods in such cases as two-class and one-class pattern classification respectively.
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  • Chung Fu-Lai, Deng Zhao-hong, and Wang Shi-tong. From minimum enclosing ball to fast fuzzy inference system training on large datasets [J].IEEE Transactions on Fuzzy System.2009, 17(1):173-184[7]Wang Di, Zhang Bo, and Zhang Peng, et al.An online core vector machine with adaptive MEB adjustment [J].Pattern Recognition.2010, 43(1):3468-3482[8]Mller, K R, Mika S, Ratsch G, Tsuda K, and Schlkopf B. An introduction to kernel-based learning algorithms [J].IEEE Transactions on Neural Networks.2001, 12(2):181-201[9]Orabona F, Castellini C, and Caputo B, et al.On-line independent support vector machines [J].Pattern Recognition.2010, 43(1):1402-1412[10]Schlkopf B, Smola A J, Williamson R, and Bartlett P L. New support vector algorithms [J].Neural Computation.2000, 12(5):1207-1245[11]Tax D M J and Duin R P W. Support vector data description [J].Machine Learning.2004, 54(1):45-66[12]Wu Mingrui and Ye Jieping. A small sphere and large margin approach for novelty detection using training data with outliers [J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2009, 31(11):2088-2092[13]邓乃扬, 田英杰. 支持向量机理论,算法与拓展[M]. 北京: 科学出版社, 2009: 5-180.[1]邓乃扬,田英杰.支持向量机理论,算法与拓展.北京:科学出版社,2009:5-180.
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