2008

A. Schwaighofer, T. Schroeter, S. Mika, K. Hansen, A. ter Laak, P. Lienau, A. Reichel, N. Heinrich, and K.-R. Müller, A Probabilistic Approach to Classifying Metabolic Stability, J. Chem. Inf. Model, March 8, 2008

D. Coppi, S. Mika, H. Herzog, Geldwäscheprävention bei Kreditinstituten: Rahmenbedingungen und Anforderungen, in Wirtschaftskriminalität als Bestandteil des Risikomanagements, Bankenverlag 2008, to appear.

2007

T. Schroeter, A. Schwaighofer, S. Mika, A. Ter Laak, D. Suelzle, U. Ganzer, N. Heinrich, and K.-R. Müller. Machine learning models for lipophilicity and their domain of applicability.Mol. Pharm., 4(4):524-538, 2007.

T. Schroeter, A. Schwaighofer, S. Mika, A. ter Laak, D. Sülzle, U. Ganzer, N. Heinrich, und K.-R. Müller. Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J Comput Aided Mol Des. 2007 Sep;21(9):485-498

T. Schroeter, A. Schwaighofer, S. Mika, A. ter Laak, D. Sülzle, U. Ganzer, N. Heinrich, und K.-R. Müller. Predicting Lipophilicity of Drug-Discovery Molecules using Gaussian Process Models. ChemMedChem. 2007 Sep 10;2(9):1265-1267

Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert MüllerAccurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach J. Chem. Inf. Model., 47(2), 2007.

2006

Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Suelzle and Nikolaus Heinrich, Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach, In: American Chemical Society 232nd National Meeting & Exposition, September 10 - 14, 2006, San Francisco.

2005

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.

2004

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.

2003

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.

2002

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)

2001

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)

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)

2000 

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.

1999 

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)

1998

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)