Transductive Support Vector Machines Using Simulated Annealing

  • Fan Sun
  • Maosong Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3801)


Transductive inference estimates classification function at samples within the test data using information from both the training and the test data set. In this paper, a new algorithm of transductive support vector machine is proposed to improve Joachims’ transductive SVM to handle various data distributions. Simulated annealing heuristic is used to solve the combinatorial optimization problem of TSVM, in order to avoid the problems of having to estimate the ratio of positive/negative samples and local optimum. The experimental result shows that TSVM-SA algorithm outperforms Joachims’ TSVM, especially when there is a significant deviation between the distribution of training and test data.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at
  2. 2.
    Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  3. 3.
    Gammerman, A., Vapnik, V., Vowk, V.: Learning by transduction. In: Conference on Uncertainty in Artificial Intelligence, pp. 148–156 (1998)Google Scholar
  4. 4.
    Ingber, L.: Simulated annealing: Practice versus theory. Mathl. Comput. Modelling 18(11), 29–57 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Joachims, T.: Transductive inference for text classification using support vector machines. In: International Conference on Machine Learning (ICML), pp. 200–209 (1999)Google Scholar
  6. 6.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of State Calculations by Fast Computing Machines. J. Chem. Phys. 21(6), 1087–1092 (1953)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Fan Sun
    • 1
  • Maosong Sun
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science & TechnologyTsinghua UniversityBeijingChina

Personalised recommendations