微信服务号

微信订阅号

2025-5-15- 14
Home > Archive>Volume 24, Issue 11, 2013 >2548-2557. DOI:10.3724/SP.J.1001.2013.04475
PDF HTML XML Export Cite reminder
Weighted Smooth CHKS Twin Support Vector Machines
Author:
Affiliation:

  • Article
    • | |
  • Metrics
    • |
  • Reference [19]
    • |
  • Related
    • |
  • Cited by [4]
    • | |
  • Comments
    Abstract:

    Smooth twin support vector machines (STWSVM) uses Sigmoid function to transform the unsmooth twin support vector machines (TWSVM) into smooth ones. However, because of the low approximation ability of Sigmoid function, the classification accuracy of STWSVM is unsatisfactory. Furthermore, similar to TWSVM, STWSVM is sensitive to the abnormal samples. In order to address the above problems, this paper introduces CHKS function, and proposes a smooth twin support vector machines, smooth CHKS twin support vector machines (SCTWSVM). In order to reduce the influence of abnormal samples on SCTWSVM, different importance are given for each training sample according to the sample point positions for SCTWSVM, resulting in weighted smooth CHKS twin support vector machines (WSCTWSVM). The study proves that SCTWSVM is not only strictly convex, but also can meet the arbitrary order smooth performance. Meanwhile, the experimental results show that SCTWSVM has better performance than STWSVM. Furthermore, the experimental results also show that WSCTWSVM is effective and feasible relative to SCTWSVM.

    Reference
    [1] Zeng ZQ, Gao J. Simplified support vector machine based on reduced vector set method. Ruan Jian Xue Bao/Journal of Software, 2007,18(11):2719-2727 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18/2719.htm[doi: 10.1360/jos1827 19]
    [2] Ding SF, Qi BJ. Research of granular support vector machine. Artificial Intelligence Review, 2012,38(1):1-7.[doi: 10.1007/s10462-011-9235-9]
    [3] Shi FQ, Xu J. Emotional cellular-based multi-class fuzzy support vector machines on product''s KANSEI extraction. Applied Mathematics & Information Sciences, 2012,6(1):41-49.
    [4] Ding SF, Jin FX, Zhao XW. Modern Data Analysis and Information Pattern Recognition. Beijing: Science Press, 2013. 35-38 (in Chinese).
    [5] Ding SF, Xu L, Su CY, Jin FX. An optimizing method of RBF neural network based on genetic algorithm. Neural Computing and Applications, 2012,21(2):333-336.[doi: 10.1007/s00521-011-0702-7]
    [6] Ding SF, Jia WK, Su CY, Zhang LW, Liu LL. Research on neural network algorithm based on fact or analysis and cluster analysis. Neural Computing and Applications, 2011,20(2):297-302.[doi: 10.1007/s00521-010-0416-2]
    [7] Cortes C, Vapnik VN. Support vector networks. Machine Learning, 1995,20:273-297.[doi: 10.1007/BF00994018]
    [8] Osuna E, Freund R. An improved training algorithm for support vector machines. In: Proc. of the 1997 IEEE Workshop on Neural Networks for Signal Processing. New York: IEEE Press, 1997. 276-285.[doi: 10.1109/NNSP.1997.622408]
    [9] Platt JC. Using analytic QP and sparseness to speed training of support vector machines. In: Kearns M, Solla S, Cohn D, eds. Advances in Neural Information Processing Systems 11. Cambridge: MIT Press, 1999. 557-563.
    [10] Fung G, Mangasarian OL. Proximal support vector machine classifiers. In: Proc. of the 7th ACM SIFKDD Int''l Conf. on Knowledge Discovery and Data Mining. New York: IEEE Press, 2001. 77-86.[doi: 10.1145/502512.502527]
    [11] Mangasarian OL, Wild EW. Multi-Surface proximal support vector machine classification via generalized eigenvalues. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2006,28(1):69-74.[doi: 10.1109/TPAMI.2006.17]
    [12] Jayadeva KR, Suresh C. Twin support vector machines for pattern classification. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2007,29(5):905-910.[doi: 10.1109/TPAMI.2007.1068]
    [13] Cong HH, Yang CF, Pu XR. Efficient speaker recognition based on multi-class twin support vector machines and GMMs. In: Proc. of the 2008 IEEE Conf. on Robotics, Automation and Mechatronics. Piscataway: IEEE Press, 2008. 348-352.[doi: 10.1109/RAMECH.2008.4681433]
    [14] Zhang XS, Gao XB, Wang Y. Twin support tensor machine for MCS detection. Journal of Electronics (China), 2009,26(3): 318-325.[doi: 10.1007/s11767-007-0211-0]
    [15] Zhang XS, Gao XB. MCs detection approach using bagging and boosting based twin support vector machine. In: Proc. of the 2009 IEEE Int''l Conf. on Systems, Man, and Cybernetics. Piscataway: IEEE Press, 2009. 5000-5005.[doi: 10.1109/ICSMC.2009.53463 75]
    [16] Kumar MA, Gopal M. Application of smoothing technique on twin support vector machines. Pattern Recognition Letters, 2008, 29(13):1842-1848.[doi: 10.1016/j.patrec.2008.05.016]
    [17] Wu Q. Research on learning algorithms for support vector machines based on optimization theory[Ph.D. Thesis]. Xi''an: Xidian University in Candidacy, 2009 (in Chinese).
    [18] Yuan YX, Sun WY. Optimization Theory and Methods. Beijing: Science Press, 1997. 45-49 (in Chinese).
    [19] Kumar MA, Gopal M. Least squares twin support vector machines for pattern classification. Expert Systems with Applications, 2009,36(4):7535-7543.[doi: 10.1016/j.eswa.2008.09.066]
    Related
    Comments
    Comments
    分享到微博
    Submit
Get Citation

丁世飞,黄华娟,史忠植.加权光滑CHKS孪生支持向量机.软件学报,2013,24(11):2548-2557

Copy
Share
Article Metrics
  • Abstract:6205
  • PDF: 7659
  • HTML: 0
  • Cited by: 0
History
  • Received:April 24,2013
  • Revised:August 02,2013
  • Online: November 01,2013
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the first2044656Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063