Context-Sensitive Kernel Functions: A Distance Function Viewpoint

  • Bram Vanschoenwinkel
  • Feng Liu
  • Bernard Manderick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in different kernel functions and how these kernel functions correspond to real kernels between the continuous, real representations of the symbolic, context-based representations of the vectors.


Support Vector Machine Kernel Function Orthonormal Vector String Data Splice Site Prediction 
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.


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  1. 1.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)Google Scholar
  2. 2.
    Schölkopf, B.: The kernel trick for distances. Technical report, Microsoft Research (2000)Google Scholar
  3. 3.
    Vanschoenwinkel, B.: Substitution matrix based kernel functions for protein secondary structure prediction. In: Proceedings of ICMLA 2004 (International Conference on Machine Learning and Applications) (2004)Google Scholar
  4. 4.
    Vanschoenwinkel, B., Manderick, B.: Appropriate kernel functions for support vector machine learning with sequences of symbolic data. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Deterministic and Statistical Methods in Machine Learning. LNCS (LNAI), vol. 3635, pp. 256–280. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  6. 6.
    Vanschoenwinkel, B., Liu, F., Manderick, B.: Weighted kernel functions for svm learning in string domains: A distance function viewpoint. In: International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, China. Vrije Universiteit Brussel, Belgium (2005)Google Scholar
  7. 7.
    Berg, C., Christensen, J.P.R., Ressel, P.: Harmonic Analysis on Semigroups. Springer, Heidelberg (1984)MATHGoogle Scholar
  8. 8.
    Vanschoenwinkel, B., Manderick, B.: Context-sensitive kernel functions: A comparison between different context weights. In: Belgisch Nederlandse Artifcial Intelligence Conference (BNAIC), Brussels. Vrije Universiteit Brussel, Belgium (2005)Google Scholar
  9. 9.
    Chih-Chung, C., Chi-Jen, L.: LIBSVM: A Library for Support Vector Machines (2004)Google Scholar
  10. 10.
    Daelemans, W., Zavrel, J., Berck, S.: Mbt: A memorybased part of speech tagger-generator (1996)Google Scholar
  11. 11.
    Noreen, E.W.: Computer-Intensive Methods for Testing Hypotheses. John Wiley & Sons, Chichester (1989)Google Scholar
  12. 12.
    Degroeve, S.: Design and Evaluation of a Linear Classification Strategy for Gene Structural Element Recognition. PhD thesis, Universiteit Gent, Faculty of Sciences, Gent, Belgium (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bram Vanschoenwinkel
    • 1
  • Feng Liu
    • 1
  • Bernard Manderick
    • 1
  1. 1.Computational Modeling LabVrije Universiteit BrusselBrusselBelgium

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