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S. Mika, G. Rätsch, and K.-R. Müller. A mathematical programming approach to the Kernel Fisher algorithm. In T.K. Leen, T.G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 591-597. MIT Press, 2001. (PDF)
S. Mika, A.J. Smola, and B. Schölkopf. An improved training algorithm for kernel fisher discriminants. In T. Jaakkola and T. Richardson, editors, Proceedings AISTATS 2001, pages 98-104, San Francisco, CA, 2001. Morgan Kaufmann. (PDF)
K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-201, 2001.
G. Rätsch, S. Mika, and M.K. Warmuth. On the convergence of leveraging. NeuroCOLT2 Technical Report 98, Royal Holloway College, London, August 2001. (PDF)
A.J. Smola, S. Mika, B. Schölkopf, and R.C. Williamson. Regularized principal manifolds. Journal of Machine Learning Research, 1:179-209, June 2001. (PDF)
S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors, Neural Networks for Signal Processing IX, pages 41-48. IEEE, 1999. (PDF)
S. Mika, B. Schölkopf, A.J. Smola, K.-R. Müller, M. Scholz, and G. Rätsch. Kernel PCA and de-noising in feature spaces. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 536-542. MIT Press, 1999. (PDF)
G. Rätsch, B. Schölkopf, A.J. Smola, S. Mika, T. Onoda, and K.-R. Müller. Robust ensemble learning. In A.J. Smola, P.L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 207-219. MIT Press, Cambridge, MA, 1999. (PDF)
B. Schölkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A.J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017, September 1999. (PDF)
A.J. Smola, S. Mika, B. Schölkopf, and R.C. Williamson. Regularized principal manifolds. Journal of Machine Learning Research, 1999.
A.J. Smola, R.C. Williamson, S. Mika, and B. Schölkopf. Regularized principal manifolds. In Paul Fischer and Hans Ulrich Simon, editors, Proceedings of EuroCOLT 99), volume 1572 of LNAI, pages 214-229, Berlin, March 1999. Springer.
A. Zien, G. Rätsch, S. Mika, C. Lemmen B. Schölkopf, A.J. Smola, T. Lengauer, and K.-R. Mueller. Engineering support vector machine kernel that recognize translation initiation sites in DNA. In Proceedings GCB'99, 1999. (PDF)
K. Tsuda, G. Rätsch, , S. Mika, and K.-R. Müller. Learning to predict the leave-one-out error of kernel based classifiers. In Proc. ICANN'01, 2001. (PDF)
S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A.J. Smola, and K.-R. Müller. Invariant feature extraction and classification in kernel spaces. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 526-532. MIT Press, 2000. (PDF)
S. Mika, A.J. Smola, and B. Schölkopf. An improved training algorithm for kernel fisher discriminants. MSR-TR-2000- 77, Microsoft Research, Cambridge, UK, 2000. AISTATS 2001.
G. Rätsch, B. Schölkopf, S. Mika, and K.-R. Müller. Svm and boosting: One class. Technical Report 119, GMD FIRST, Berlin, November 2000. (PDF)
G. Rätsch, B. Schölkopf, A.J. Smola, S. Mika, T. Onoda, and K.-R. Müller. Robust ensemble learning for data mining. In Proceedings of PAKDD 2000, Lecture Notes in Artificial Intelligence, Springer, April 2000. (PDF)
G. Rätsch, B. Schölkopf, A.J. Smola, K.-R. Müller, T. Onoda, and S. Mika. nu -Arc: Ensemble learning in the presence of outliers. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 561-567. MIT Press, 2000. (PDF)
G. Rätsch, M. Warmuth, S. Mika, T. Onoda, S. Lemm, and K.-R. Müller. Barrier boosting. In Proceedings COLT, pages 170-179, San Francisco, February 2000. Morgan Kaufmann. (PDF)
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer, and K.-R. Müller. Engineering support vector machine kernels that recognize translation initiation sites in DNA. Bioinformatics, 16(9):799-807, September 2000.
S. Mika. Kernel algorithms for nonlinear signal processing in feature spaces. Master's thesis, Technical University of Berlin, November 1998. (PDF)
B. Schölkopf, S. Mika, A.J. Smola, G. Rätsch, and K.-R. Müller. Kernel PCA pattern reconstruction via approximate pre-images. In L. Niklasson, M. Bodén, and T. Ziemke, editors, Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing, pages 147 -- 152, Berlin, 1998. Springer Verlag. (PDF)
A.J. Smola, S. Mika, and B. Schölkopf. Quantization functionals and regularized principal manifolds. Technical Report NC-TR-98-028, Royal Holloway College, University of London, UK, 1998. (PDF)
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