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Machine Learning

, Volume 46, Issue 1–3, pp 5–9 | Cite as

Editorial: Kernel Methods: Current Research and Future Directions

  • Nello Cristianini
  • Colin Campbell
  • Chris Burges
Editorial Board

References

  1. Bennett, K., Cristianini, N., Shawe-Taylor, J., & Wu, D. (2000). Enlarging the margin in perceptron decision trees. Machine Learning, 41, 295-313.Google Scholar
  2. Bosen, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In D. Haussler (Ed.), Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (pp. 144-152). Pittsburgh, PA: ACM Press.Google Scholar
  3. Burges, C. (1996). Simplified support vector decision rules. In L. Saitta (Ed.), Proceedings of the Thirteenth International Conference on Machine Learning (pp. 71-77). Bari, Italy: Morgan Kaufman.Google Scholar
  4. Burges, C. & Crisp, D. (2000). Uniqueness of the svm solution. NIPS, 12.Google Scholar
  5. Cristianini, N. & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge, UK: Cambridge University Press. www.support-vector.net.Google Scholar
  6. Schapire, R., Freund, Y., Bartlett, P., & Lee, W. S. (to appear). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics. An earlier version appeared in D. H. Fisher, Jr. (Ed.), Proceedings ICML97, Morgan Kaufmann.Google Scholar
  7. Schölkopf, B., & Burges, C. J. C., & Smola, A. J. (1999). Advances in kernel methods-Support vector learning. Cambridge, MA: MIT Press, 1999.Google Scholar
  8. Schölkopf, B. & Smola, A. J. (to be published). Learning with kernels. Cambridge, MA: MIT Press.Google Scholar
  9. Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C., & Anthony, M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory.Google Scholar
  10. Smola, A., Bartlett, P., Schölkopf, B., & Schuurmans, C. (2000). Advances in large margin classifiers. Cambridge, MA: MIT Press.Google Scholar
  11. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.Google Scholar
  12. Williams, C. K. I. (1998). Prediction with gaussian processes: From linear regression to linear prediction and beyond. In M. I. Jordan (Ed.), Learning and inference in graphical models. Dordrecht: Kluwer.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Nello Cristianini
  • Colin Campbell
  • Chris Burges

There are no affiliations available

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