[Home] - [2003] - [2002] - [2001] - [2000] - [1999] - [1998] - [1997]

2003 (Top)

[1]

K. Tsuda and G. Rätsch. Image reconstruction by linear programming. Technical report, Max-Planck Institute for biological Cybernetics, Tübingen, October 2003. A shorter version will appear in the NIPS'03 proceedings.

[2]

Robust multi-class boosting. In EuroSpeech, Geneva, September 2003. IEEE. Invited paper to EuroSpeech special session on Machine Learning.

[3]

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 space. IEEE PAMI, 25(5):623-633, 2003.

[4]

G. Rätsch, A.J. Smola, and S. Mika. Adapting codes and embeddings for polychotomies. In Advances in Neural information processings systems, volume 15, 2003. Slides of the talk at the main conference: PS,PDF.

[5]

M.K. Warmuth, J. Liao, G. Rätsch, M. Mathieson, S. Putta, and C. Lemmen. Active learning with svms in the drug discovery process. Chemical Information and Computer Sciences, 43(2):667-673, 2003.

[6]

R. Meir and G. Rätsch. An introduction to boosting and leveraging. In S. Mendelson and A. Smola, editors, Advanced Lectures on Machine Learning, LNCS, pages 119-184. Springer, 2003. In press. Copyright by Springer Verlag. (PDF)

2002 (Top)

[1]

G. Rätsch. Robustes Boosting durch konvexe Optimierung. In D. Wagner et al., editor, Ausgezeichnete Informatikdissertationen 2001, volume D-2 of GI-Edition -- Lecture Notes in Informatics (LNI), pages 125-136. Bonner Köllen, 2002. In press. In German. (PostScript)

[2]

G. Rätsch and M.K. Warmuth. Marginal boosting. In Proceedings of the Annual Conference on Computational Learning Theory, February 2002. Slides of the Talk. Copyright by Springer. Extended version submitted to JMLR. (PostScript) (PDF)

[3]

S. Sonnenburg, G. Rätsch, A. Jagota, and K.R. Müller. New methods for splice site recognition. In Proceedings of the International Conference on Artifical Neural Networks, February 2002. Details, data, results. Copyright by Springer. This paper reduced my Erdös-number to two (Jagota,Erdös). (PostScript) (PDF)

[4]

K. Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, and K.R. Müller. A new discriminative kernel from probabilistic models. Neural Computation, 2002. In press. (PostScript) (PDF)

[5]

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 processings systems, volume 14, 2002. Longer version also NeuroCOLT Technical Report NC-TR-2001-098. (PostScript) (PDF)

[6]

K. Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, and K.R. Müller. A new discriminative kernel from probabilistic models. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural information processings systems, volume 14, 2002. (PostScript) (PDF)

[7]

M.K. Warmuth, G. Rätsch, M. Mathieson, J. Liao, and C. Lemmen. Active learning in the drug discovery process. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural information processings systems, volume 14, 2002. (PostScript) (PDF)

[8]

G. Rätsch, S. Mika, B. Schölkopf, and K.-R. Müller. Constructing boosting algorithms from SVMs: an application to one-class classification. IEEE TPAMI, 24(9), September 2002. Earlier version is GMD TechReport No. 119, 2000. (PostScript) (PDF)

[9]

G. Rätsch, A. Demiriz, and K. Bennett. Sparse regression ensembles in infinite and finite hypothesis spaces. Machine Learning special issue on ``New Methods for Model Selection and Model Combination'', 48:193-221, 2002. (PostScript) (PDF)

2001 (Top)

[1]

G. Rätsch. Robust Boosting via Convex Optimization. PhD thesis, University of Potsdam, Department of Computer Science, Potsdam, Germany, October 2001. (PostScript) (PDF)

[2]

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, Proc. NIPS 13, volume 13, pages 591-597. MIT Press, 2001. (PostScript) (PDF)

[3]

K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Neural Networks, 12(2):181-201, May 2001. (PDF)

[4]

T. Onoda, G. Rätsch, and K.R. Müller. An arcing algorithm with an intuitive learning control parameter. Journal of the Japanese Society for AI, 16(5C):417-426, September 2001. in Japanese, a similar version in english appeared in Advances in Large Margin Classifiers. (PDF)

[5]

G. Rätsch, S. Mika, and M.K. Warmuth. On the convergence of leveraging. NeuroCOLT2 Technical Report 98, Royal Holloway College, London, August 2001. Shorter version accepted at NIPS*01. (PDF)

[6]

G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for AdaBoost. Machine Learning, 42(3):287-320, March 2001. also NeuroCOLT Technical Report NC-TR-1998-021.

[7]

G. Rätsch and M.K. Warmuth. Marginal boosting. NeuroCOLT2 Technical Report 97, Royal Holloway College, London, July 2001. (PDF)

[8]

K. Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, and K.R. Müller. A new discriminative kernel from probabilistic models. submitted, 2001.

[9]

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. (PostScript) (PDF)

2000 (Top)

[1]

G. Rätsch, B. Schölkopf, S. Mika, and K.-R. Müller. SVM and Boosting: One class. Technical Report 119, GMD FIRST, Berlin, November 2000. (PostScript) (PDF)

[2]

G. Rätsch, A. Demiriz, and K. Bennett. Sparse regression ensembles in infinite and finite hypothesis spaces. NeuroCOLT2 technical report, Royal Holloway College, London, September 2000. accepted for publication in the Machine Learning journal special issue on ``New Methods for Model Selection and Model Combination''. (PostScript) (PDF)

[3]

G. Rätsch, M. Warmuth, S. Mika, T. Onoda, S. Lemm, and K.-R. Müller. Barrier boosting. In Proc. COLT'00, pages 170-179, Palo Alto, February 2000. Morgan Kaufmann. This paper reduced my Erdös-number to three (Warmuth,Jagota,Erdös). (PostScript) (PDF)

[4]

J. Kohlmorgen, S. Lemm, G. Rätsch, and K.-R. Müller. Analysis of nonstationary time series by mixtures of self-organizing predictors. In Proc. NNSP'2000, pages 85-94, Sydney, 2000. IEEE. (PostScript) (PDF)

[5]

S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A.J. Smola, and K.-R. Müller. Learning discriminative and invariant nonlinear features. unpublished manuscript, 2000.

[6]

T. Onoda, G. Rätsch, and K.-R. Müller. An asymptotical analysis and improvement of AdaBoost in the binary classification case. Journal of the Japanese Society for AI, 15(2):287-296, March 2000. In japanese.

[7]

T. Onoda, G. Rätsch, and K.-R. Müller. A non-intrusive monitoring system for household electric appliances with inverters. In Proc. of NC'2000, Berlin, 2000. (PostScript) (PDF)

[8]

T. Onoda and G. Rätsch. Trends in boosting research and applications. CRIEPI Report ER 99001, Central Research Institute of the Electric Power Industry, Tokyo, March 2000.

[9]

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, Proc. of the NIPS*98 Workshop on Large Margin Classifiers: Advances in Large Margin Classifiers, pages 207-219. MIT Press, Cambridge, MA, 2000. similarly appeared in the Journal of the Japanese Society of AI, 2001. (PostScript) (PDF)

[10]

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. This is a short version of [9]. (PostScript) (PDF)

[11]

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, Proc. NIPS 12, pages 561-567. MIT Press, 2000. (PostScript) (PDF)

[12]

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, Proc. NIPS 12, pages 526-532. MIT Press, 2000. (PostScript) (PDF)

[13]

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, Sep 2000. (PostScript) (PDF)

1999 (Top)

[1]

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, Proc. NNSP'99, pages 41-48. IEEE, 1999. (PostScript) (PDF)

[2]

A. Smola, B. Schölkopf, and G. Rätsch. Linear programs for automatic accuracy control in regression. In Proc. ICANN'99, Berlin, 1999. Springer. (PostScript) (PDF)

[3]

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. (PostScript) (PDF)

[4]

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. (PostScript) (PDF)

[5]

W. Schubert, A. Koutzevlov, E. Horn, G. Rätsch, and A. Tschapek. Aspekte der Flexibilisierung von Systemen für den Hardwaretest. Technical report, University of Potsdam, Potsdam, 1999.

[6]

G. Rätsch, T. Onoda, and K.-R. Müller. Regularizing AdaBoost. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Proc. NIPS 11, pages 564-570. MIT Press, 1999. (PostScript) (PDF)

[7]

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, Proc. NIPS 11, pages 536-542. MIT Press, 1999. (PostScript) (PDF)

1998 (Top)

[1]

G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for adaboost. NeuroCOLT Technical Report NC-TR-1998-021, Royal Holloway College, University of London, UK, 1998. to appear in Machine Learning. (PostScript) (PDF)

[2]

G. Rätsch, T. Onoda, and K.-R. Müller. An improvement of adaboost to avoid overfitting. In Proc. ICONIP, pages 506-509, Kitakyushu, Japan, May 1998. (PostScript) (PDF)

[3]

G. Rätsch. Ensemble learning methods for classification. Master's thesis, Dep. of Computer Science, University of Potsdam, April 1998. In German. (PostScript)

[4]

K.-R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik. Using support vector machines for time series prediction. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Proc. of the NIPS Workshop on Support Vectors. MIT Press, Cambridge, MA, 1998. (PostScript) (PDF)

[5]

T. Onoda, G. Rätsch, and K.-R. Müller. An asymptotic analysis of AdaBoost in the binary classification case. In L. Niklasson, M. Bodén, and T. Ziemke, editors, Proc. ICANN'98, pages 195-200, March 1998. (PostScript) (PDF)

[6]

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. (PostScript) (PDF)

1997 (Top)

[1]

K.-R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik. Using support vector machines for time series prediction. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors, Proc. ICANN'97, volume 1327 of LNCS, pages 999-1004, Berlin, 1997. Springer. (PostScript) (PDF)

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