Kernel Methods are a class of algorithms for pattern analysis with a number of convenient features. They can deal in a uniform way with a multitude of data types and can be used to detect many types of relations in data. Importantly for applications, they have a modular structure, in that any kernel function can be used with any kernel-based algorithm. This means that customized solutions can be easily developed from a standard library of kernels and algorithms. This paper demonstrates a case study in which many algorithms and kernels are mixed and matched, for a cross-language text analysis task. All the software is available online.


Partial Little Square Canonical Correlation Analysis Kernel Method Spectral Cluster Kernel 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.


  1. 1.
    Bach, F.R., Jordan, M.I.: Kernel independent component analysis. Journal of Machine Learning Research 3, 1–48 (2002)CrossRefMathSciNetGoogle Scholar
  2. 2.
    De Bie, T., Cristianini, N., Rosipal, R.: Eigenproblems in pattern recognition. In: Bayro-Corrochano, E. (ed.) Handbook of Computational Geometry for Pattern Recognition, Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  4. 4.
    Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification, 193–218 (1985)Google Scholar
  5. 5.
    Jaakkola, T., Diekhans, M., Haussler, D.: Using the fisher kernel method to detect remote protein homologies. In: Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (1999)Google Scholar
  6. 6.
    Kashima, H., Tsuda, K., Inokuchi, A.: Kernel methods in computational biology. In: Schoelkopf, B., Tsuda, K., Vert, J.P. (eds.) Handbook of Computational Geometry for Pattern Recognition, Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Kondor, R.I., Lafferty, J.: Diffusion kernels on graphs and other discrete structures. In: Proceedings of the ICML (2002)Google Scholar
  8. 8.
    Leslie, C., Kuang, R.: Fast kernels for inexact string matching. In: Conference on Learning Theory and Kernel Workshop, COLT 2003 (2003)Google Scholar
  9. 9.
    Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.-R.: Fisher discriminant analysis with kernels. In: Hu, Y.-H., Larsen, J., Wilson, E., Douglas, S. (eds.) Neural Networks for Signal Processing IX, pp. 41–48. IEEE, Los Alamitos (1999)CrossRefGoogle Scholar
  10. 10.
    Ng, A., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, Cambridge, MA, vol. 14, MIT Press, Cambridge (2002)Google Scholar
  11. 11.
    Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  12. 12.
    Shawe-Taylor, J., Cristianini, N.: Kernel methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  13. 13.
    Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recognition Letters 20(11-13), 1191–1199 (1999)CrossRefGoogle Scholar
  14. 14.
    Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., Müller, K.-R.: A new discriminative kernel from probabilistic models. Neural Computation 14(10), 2397–2414 (2002)zbMATHCrossRefGoogle Scholar
  15. 15.
    Vert, J.-P., Kanehisa, M.: Graph-driven features extraction from microarray data using diffusion kernels and kernel cca (2003)Google Scholar
  16. 16.
    Vinokourov, A., Cristianini, N., Shawe-Taylor, J.: Inferring a semantic representation of text via cross-language correlation analysis (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tijl De Bie
    • 1
  • Nello Cristianini
    • 2
  1. 1.K.U.Leuven, ESAT-SCDLeuvenBelgium
  2. 2.Statistics Dept.U.C.DavisDavisUSA

Personalised recommendations