Skip to main content
SpringerLink
Log in

A New Classification Method Based on Semi-supervised Support Vector Machine

  • Conference paper
  • First Online:
Human Centered Computing (HCC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8944))

Included in the following conference series:

Abstract

Semi-supervised learning using tag vector machine is a relatively new method of data classification and label-free. Semi-supervised support vector machines model the objective function is not smooth and fast optimization algorithm to solve the model cannot be applied. This paper presents a general three-moment method 3 times differentiable at the origin of construct quintic spline functions, construction of hinge can be used to approximate symmetry loss functions, the approximate accuracy estimation of and quintic spline functions. And on top of this, deduced five and a half times b-spline smoothing support vector machines for non-smooth a-smoothing model analyses the convergence. Broyden-Fletcher-Goldfarb-Shanno (storage) algorithm can be used in new models. Experimental results show that the new model has a better performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Deng, N.Y., Tian, Y.J.: New Method in Data Mining: Support Vector Machine. Science Press, Beijing (2004)

    Google Scholar 

  2. Miller, D.J., Uyar, H.S.: A mixture of experts classifier with learning based on both labelled and unlabelled data. In: Proc. NIPS 9, pp. 571–577 (1997)

    Google Scholar 

  3. Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization Techniques for Semi - Supervised Support Vector Machines. Journal of Machine Learning Research 9, 203–233 (2008)

    MATH  Google Scholar 

  4. Sun, L., Jing, L., Xia, X.: A new proximal support vector machine for semi-supervised classification. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1076–1082. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Reddy, S., Shevade, S., Murty, M.N.: A Fast Quasi-Newton Method for Semi-Supervised SVM. Pattern Recognition 44, 2305–2313 (2011)

    Article  MATH  Google Scholar 

  6. Angelini, L., Marinazzo, D., Pellicoro, M., Stramaglia, S.: Semi-supervised Learning by Search of Optimal Target Vector. Pattern Recognition Letters 29, 34–39 (2008)

    Article  Google Scholar 

  7. Kalakech, M., Biela, P., Macaire, L., Hamad, D.: Constraint Scores for Semi-supervised Feature Selection: A comparative study. Pattern Recognition Letters 32, 656–665 (2011)

    Article  Google Scholar 

  8. Liu, Y.Q., Liu, S.Y., Gu, M.T.: Polynomial Smooth Classification Algorithm of Semi-supervised Support Vector Machines. Computer Science 36(7), 179–181 (2009)

    Google Scholar 

  9. Astorino, A., Fuduli, A.: Nonsmooth optimization techniques for semi-supervised classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12), 2135–2142 (2007)

    Article  Google Scholar 

  10. Liao, D., Jiang, B., Wei, X., Li, X., Zhuang, Z.: Fast learning algorithm with progressive transductive support vector machine. Systems Engineering and Electronics 29, 87–91 (2007)

    MATH  Google Scholar 

  11. Chaplle, O., Zien, A.: Semi-supervised classification by low density separation. In: The 10th International Workshop on Artificial Intelligence and Statistics, Barbados (2005)

    Google Scholar 

  12. Zhu, X., Ghahramani, Z., Lafferty, J.: Semisupervised learning using gaussian Fields and harmonic functions. In: Proc. 20th Int. Conf. on Mach. Learn., pp. 912–919 (2003)

    Google Scholar 

  13. Lee, Y.J., Mangasarian, O.L.: SSVM: A Smooth Support Vector Machine for Classification. Computational Optimization and Applications 22(1), 5–22 (2001)

    Article  MathSciNet  Google Scholar 

  14. Yuan, Y.B., Yan, J., Xu, C.X.: Polynomial Smooth Support Vector Machine. Chinese Journal of Computers 28(1), 9–17 (2005)

    Google Scholar 

  15. Musicant, D.R., Feinberg, A.: Active set support vector regression. IEEE Trans. Neural Net-works 15(2), 268–275 (2004)

    Article  Google Scholar 

  16. Park, S., Zhang, B.: Co-trained support vector machines for large scale unstructured document classification using unlabeled data and syntactic information. Inform. Process. Manag. 40(3), 421–439 (2004)

    Article  Google Scholar 

  17. Wu, J.: Support Vector Machines learning Algorithm Research Based on Optimization Theory. Xian, University of Electronic Science and Technology Xian, Applied Mathematics (2009)

    Google Scholar 

  18. Zhang, X.D., Shao, S., Liu, Q.S.: Smooth support vecter machine model based on spline function. Jornal of University of Science and Tecnology Beijing 34(6), 718–725 (2012)

    MATH  Google Scholar 

  19. Xiong, J.Z., Yuan, H.Q., Peng, H.: A General Formulation of Polynomial Smooth Support Vector Machines. Journal of Computer Research and Development 45(8), 1346–1353 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Hunan University of Commerce, Changsha, China

    Weijin Jiang & Xu Yuhui

  2. Department of Computer, Hunan Radio and TV University, South of Furong Road, Changsha, 410005, Hunan, China

    Yao Lina & Jiang Xinjun

Authors
  1. Weijin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yao Lina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiang Xinjun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xu Yuhui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yao Lina .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe, Middlesex, United Kingdom

    Bo Hu

  3. Fudan University, Shanghai, China

    Ning Gu

  4. Telecommunications and Information Communication Technology, National Institute of Posts, Phnom Penh, Cambodia

    Sopheap Seng

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Jiang, W., Lina, Y., Xinjun, J., Yuhui, X. (2015). A New Classification Method Based on Semi-supervised Support Vector Machine. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-15554-8_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15553-1

  • Online ISBN: 978-3-319-15554-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation