Parameterized Semi-supervised Classification Based on Support Vector for Multi-relational Data

  • Ling Ping
  • Zhou Chun-Guang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


A Parameterized Semi-supervised Classification algorithm based on Support Vector (PSCSV) for multi-relational data is presented in this paper. PSCSV produces class contours with support vectors, and further extracts center information of classes. Data is labeled according to its affinity to class centers. A novel Kernel function encoded in PSCSV is defined for multi-relational version and parameterized by supervisory information. Another point is the self learning of penalty parameter and Kernel scale parameter in the support-vector-based procedures, which eliminates the need to search parameter spaces. Experiments on real datasets demonstrate performance and efficiency of PSCSV.


Penalty Parameter Class Center Main Table Supervisory Information Elementary Kernel 
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  1. 1.
    Ďzeroski, S.: Multi-Relational Data Mining: An Introduction. ACM SIGKDD Explorations Newsletter 5(1) (2003)Google Scholar
  2. 2.
    Ben-Hur, A., Horn, D., Siegelmann, H.T.: Support Vector Clustering. Journal of Machine Learning Research, 125–137 (2001)Google Scholar
  3. 3.
    Kecman, V.: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models. The MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  4. 4.
    Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Berlin, Heidelberg, New York (2005)Google Scholar
  5. 5.
    Xing, E., Ng, A., Jordan, M.: Distance Metric Learning, with Application to Clustering with Side-information. Advances in Neural Information Processing Systems 15, 505–512 (2003)Google Scholar
  6. 6.
    Jong, K., Marchiori, E., van der Vaart, A.: Finding Clusters using Support Vector Classifiers. In: ESANN 2003 proceedings - European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 223–228 (2003)Google Scholar
  7. 7.
    Haussler, D.: Convolution Kernels on Discrete Structures. Technical report, Department of Computer Science, University of California, Santa Cruz (1999)Google Scholar
  8. 8.
  9. 9.
    Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31–71 (1997)zbMATHCrossRefGoogle Scholar
  10. 10.
  11. 11.
    Bloedorn, E., Michalski, R.: Data driven constructive induction. IEEE Intelligent Systems 13(2), 30–37 (1998)CrossRefGoogle Scholar
  12. 12.
    Gaertner, T., Flach, P., Kowalczyk, A., Smola, A.: Multi-instance kernels. In: Sammut, C. (ed.) ICML 2002, Morgan Kaufmann, San Francisco (2002)Google Scholar
  13. 13.
    Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans.on Neural Networks 13(3), 780–784 (2002)CrossRefGoogle Scholar
  14. 14.
    Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, MIT Press, Cambridge (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Ping
    • 1
    • 2
  • Zhou Chun-Guang
    • 1
  1. 1.College of Computer ScienceJilin University, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of EducationChangchunChina
  2. 2.School of Computer ScienceXuzhou Normal UniversityXuzhouChina

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