SVMV – A Novel Algorithm for the Visualization of SVM Classification Results

  • Xiaohong Wang
  • Sitao Wu
  • Xiaoru Wang
  • Qunzhan Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, a novel algorithm, called support vector machine visualization (SVMV), is proposed. The SVMV algorithm is based on support vector machine (SVM) and self-organizing mapping (SOM). High dimensional data and binary classification results can be visualized in a low dimensional space. Compared with other traditional visualization algorithms like SOM and Sammon’s mapping algorithm, the SVMV algorithm can deliver better visualization on classification results. Experimental results corroborate the effectiveness and usefulness of SVMV.


Support Vector Machine Classification Boundary Support Vector Machine Classification Visualization Algorithm Bias Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jackson, J.E.: A User’s Guide to Principal Components. John Wiley & Sons, New York (1991)MATHCrossRefGoogle Scholar
  2. 2.
    Cox, T.C., Cox, M.A.A.: Multidimensional Scaling, 2nd edn. Chapman & Hall/CRC, Boca Raton (2000)CrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Heidelberg (1997)MATHGoogle Scholar
  4. 4.
    Sammon, J.W.: A Nonlinear Mapping for Data Structure Analysis. IEEE Trans. on Computers 18(5), 401–409 (1969)CrossRefGoogle Scholar
  5. 5.
    Demartines, P., Hérault, J.: Curvilinear Component Analysis: A Self-Organizing Neural Network for Nonlinear Mapping of Data Sets. IEEE Trans. on Neural Networks 8(1), 148–154 (1997)CrossRefGoogle Scholar
  6. 6.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Non-linear Dimensionality Reduction. Science 290(12), 2319–2323 (2000)CrossRefGoogle Scholar
  7. 7.
    Li, X.Z.: Visualization of High-Dimensional Data with Relational Perspective Map. Information Visualization 3(1), 49–59 (2004)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  9. 9.
    Schölkopf, B., Smola, A.: New Support Vector Algorithms. Neural Computation 12(5), 1207–1245 (2000)CrossRefGoogle Scholar
  10. 10.
    Wu, S.T., Chow, W.S.: Support Vector Visualization and Clustering Using Self-Organizing Map and Support Vector One-Class Classification. In: Proc. of IEEE Int. Joint Conf. on Neural Networks, Portland, USA, pp. 803–808 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaohong Wang
    • 1
  • Sitao Wu
    • 1
  • Xiaoru Wang
    • 1
  • Qunzhan Li
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduP.R. China

Personalised recommendations