|
K.-R. Müller, G. Rätsch, S. Sonnenburg, S. Mika, M. Grimm, and N. Heinrich. Classifying 'drug-likeness' with kernel-based learning methods. J. Chem. Inf. Model, 45:249-253, 2005.
J. Ham, D. Lee, S. Mika, and B. Schölkopf. Kernel view of the dimensionality reduction of manifolds. In Proceedings ICML 04, 2004.
S. Knabe, S. Mika, K.-R. Müller, G. Rätsch, and W. Schruff. Zur Beurteilung des Fraud-Risikos im Rahmen der Abschlussprüfung. Die Wirtschaftsprüfung, 19(57):1057-1067, October 2004.
S. Mika, C. Schäfer, P. Laskov, D. Tax, and K.-R.Müller. Support vector machines. In J.E. Gentle, W. Härdle, and Y. Mori, editors, Handbook of Computational Statistics. Springer, 2004.
S. Mika. Kern Fisher Diskriminanten. In D. Wagner, Editor, Ausgezeichnete Informatikdissertationen 2002, pages 69-78. Köllen Druck & Verlag GmbH, Bonn, 2003. in German.
S. Mika, G. Rätsch, J Weston, B. Schölkopf, A. Smola, and K.-R. Müller. Constructing descriptive and discriminative non-linear features: Rayleigh coefficients in kernel feature spaces. IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003.
G. Rätsch, A.J. Smola, and S. Mika. Adapting codes and embeddings for polychotomies. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing 15. MIT Press, 2003. to appear.
S. Mika. Kernel Fisher Discriminants. PhD thesis, University of Technology, Berlin, October 2002.
G. Rätsch, S. Mika, B. Schölkopf, and K.-R. Müller. Constructing boosting algorithms from svms: an application to one-class classification. IEEE PAMI, 24(9):1184-1199, September 2002. Earlier version is GMD TechReport No. 119, 200.
G. Rätsch, S. Mika, and M.K. Warmuth. On the convergence of leveraging. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14. MIT Press, 2002. (PDF)
|
