Publications of Gunnar Rätsch

2009	Bohnert, R, Behr, J, and Rätsch, G (2009). Transcript quantification with RNA-Seq data BMC Bioinformatics, 10(S13):P5. Supplementary pages http://www.biomedcentral.com/1471-2105/10/S13/P5 Printable file

Schweikert, G, Behr, J, Zien, A, Zeller, G, Ong, CS, Sonnenburg, S, and Rätsch, G (2009). mGene.web: a web service for accurate computational gene finding Nucleic Acids Research, Web Server Issue. Supplementary pages http://mgene.org/web Printable file

Zien, A, Krämer, N, Sonnenburg, S, and Rätsch, G (2009). The Feature Importance Ranking Measure In: Proc. ECML PKDD, Springer. Lecture Notes in Artificial Intelligence. Printable file

Schweikert, G, Zien, A, Zeller, G, Behr, J, Dieterich, C, Ong, CS, Philips, P, De Bona, F, Hartmann, L, Bohlen, A, Krüger, N, Sonnenburg, S, and Rätsch, G (2009). mGene: Accurate SVM-Based Gene Finding with an Application to Nematode Genomes Genome Research. Supplementary pages http://mgene.org Supplementary website http://www.fml.tuebingen.mpg.de/Members/raetsch/papers/Supplement-mGene.pdf Printable file

Naouar, N, Vandepoele, K, Lammens, T, Casneuf, T, Zeller, G, van Hummelen, P, Weigel, D, Rätsch, G, Inzé, D, Kuiper, M, De Veylder, L, and Vuylsteke, M (2009). Quantitative RNA expression analysis with Affymetrix Tiling 1.0R Arrays identifies new E2F target genes Plant Journal, 57(1):184-194. Printable file

Zeller, G, Henz, SR, Widmer, CK, Sachsenberg, T, Rätsch, G, Weigel, D, and Laubinger, S (2009). Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole genome tiling arrays Plant Journal, 58(6):1068-1082. Supplementary pages http://www.weigelworld.org/resources/microarray/at-tax Printable file

McNally, KL, Childs, KL, Bohnert, R, Davidson, RM, Zhao, K, Ulat, VJ, Zeller, G, Clark, RM, Hoen, DR, Bureau, TE, Stokowski, R, Ballinger, DG, Frazer, KA, Cox, DR, Padhukasahasram, B, Bustamante, CD, Weigel, D, Mackill, DJ, Bruskiewich, RM, Rätsch, G, Buell, CR, Leung, H, and Leach, JE (2009). Genomewide SNP variation reveals relationships among landraces and modern varieties of rice Proceedings of the National Academy of Sciences. Supplementary pages http://www.pnas.org/content/106/30/12273.full Supplementary website http://www.pnas.org/content/106/30/12273/suppl/DCSupplemental Printable file

Schultheiss, SJ, Busch, W, Lohmann, JU, Kohlbacher, O, and Rätsch, G (2009). KIRMES: Kernel-based identification of regulatory modules in euchromatic sequences Bioinformatics. Printable file

2008	Warmuth, MK, Glocer, K, and Rätsch, G (2008). Boosting Algorithms for Maximizing the Soft Margin In: Advances in Neural Information Processing Systems (NIPS'08), MIT Press. Printable file

Zeller, G, Henz, S, Laubinger, S, Weigel, D, and Rätsch, G (2008). Transcript Normalization and Segmentation of Tiling Array Data In: Pacific Symposium on Biocomputing, vol. 13, pp. 527-538, Stanford, World Scientific. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/PSBTiling Printable file

De Bona, F, Ossowski, S, Schneeberger, K, and Rätsch, G (2008). QPALMA: Optimal Spliced Alignments of Short Sequence Reads In: Bioinformatics/Proc. ECCB'08, Oxford University Press. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/qpalma Printable file

Zeller, G, Clark, R, Schneeberger, K, Bohlen, A, Weigel, D, and Rätsch, G (2008). Detecting Polymorphic Regions in Arabidopsis thaliana with Resequencing Microarrays Genome Research, 18(6):918-929. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/mppr Printable file

Sonnenburg, S, Zien, A, Philips, P, and Rätsch, G (2008). POIMs: Positional Oligomer Importance Matrices - Understanding Support Vector Machine Based Signal Detectors In: Bioinformatics/Proc. ISMB 2008, vol. 24(13), pp. i6, Oxford University Press. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/POIM Printable file

Graf, A, Bousquet, O, Rätsch, G, and Schölkopf, B (2008). Prototype Classification: Insights from Machine Learning Neural Computation. Printable file

Laubinger, S, Sachsenberg, T, Zeller, G, Busch, W, Lohmann, J, Rätsch, G, and Weigel, D (2008). Dual roles of the nuclear cap binding complex and SERRATE in pre-mRNA splicing and microRNA processing in Arabidopsis thaliana PNAS, 105(25):8795-8800. Printable file

Schultheiss, SJ, Busch, W, Lohmann, JU, Kohlbacher, O, and Rätsch, G (2008). KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences In: German Conference on Bioinformatics, ed. by A. Beyer and M. Schroeder, pp. 158-167, GI, Heidelberg, Springer Verlag. Lecture notes in Informatics. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/kirmes Printable file

Laubinger, S, Zeller, G, Henz, S, Sachsenberger, T, Widmer, C, Naouar, N, Vuylsteke, M, Schölkopf, B, Rätsch, G, and Weigel, D (2008). At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana Genome Biology, 9:R112. Supplementary pages http://www.weigelworld.org/resources/microarray/at-tax Printable file

Ben-Hur, A, Ong, CS, Sonnenburg, S, Schölkopf, B, and Rätsch, G (2008). Support Vector Machines and Kernels for Computational Biology PLoS Computational Biology, 4(10):e1000173. Supplementary pages http://svmcompbio.tuebingen.mpg.de Printable file

Schweikert, G, Widmer, C, Schölkopf, B, and Rätsch, G (2008). An empirical Analysis of Domain Adaptation Algorithms In: Proc. NIPS 2008. Advances in Neural Information Processing Systems. Supplementary pages /raetsch/suppl/genomedomainadaptation Printable file

De Bona, F, Ossowski, S, Schneeberger, K, and Rätsch, G (2008). QPALMA:: Optimal Spliced Alignments of Short Sequence Reads In: BMC Bioinformatics, vol. 9((Suppl 10)), pp. O7, BioMed Central Ltd. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/qpalma Printable file

Bohnert, R, Zeller, G, Clark, RM, Childs, KL, Ulat, V, Stokowski, R, Ballinger, D, Frazer, K, Cox, D, Bruskiewich, R, Buell, CR, Leach, J, Leung, H, McNally, KL, Weigel, D, and Rätsch, G (2008). Revealing Sequence Variation Patterns in Rice with Machine Learning Methods BMC Bioinformatics, 9(S10):O8. Supplementary pages http://www.biomedcentral.com/1471-2105/9/S10/O8 Printable file

2007

Rätsch, G and Sonnenburg, S (2007). Large Scale Hidden Semi-Markov SVMs In: Advances in Neural Information Processing Systems (NIPS'06), ed. by B. Schölkopf and J. Platt and T. Hoffman, vol. 19, pp. 1161-1168, Cambridge, MA, MIT Press. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/HSMSVM Printable file

Rätsch, G, Sonnenburg, S, Srinivasan, J, Witte, H, Müller, KR, Sommer, R, and Schölkopf, B (2007). Improving the C. elegans genome annotation using machine learning PLoS Computational Biology, 3(2):e20. Supplementary pages http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0030020 Printable file

Schulze, U, Hepp, B, Ong, CS, and Rätsch, G (2007). PALMA: mRNA to Genome Alignments using Large Margin Algorithms Bioinformatics, 23(15):1892-1900. Supplementary pages http://bioinformatics.oxfordjournals.org/cgi/content/full/23/15/1892 Printable file

Clark, R, Schweikert, G, Toomajian, C, Ossowski, S, Zeller, G, Shinn, P, Warthmann, N, Hu, T, Fu, G, Hinds, D, Chen, H, Frazer, K, Huson, D, Schölkopf, B, Nordborg, M, Rätsch, G, Ecker, J, and Weigel, D (2007). Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana Science, 317(5836):338 - 342. Printable file

Schweikert, G, Sonnenburg, S, Philips, P, Behr, J, and Rätsch, G (2007). Accurate splice site prediction using support vector machines BMC Bioinformatics, 8(Suppl. 10):S7. Supplementary pages http://www.biomedcentral.com/1471-2105/8/S10/S7 Printable file

Chechik, G, Leslie, C, Stafford Noble, W, Rätsch, G, Morris, Q, and Tsuda, K (2007). New Problems and Methods in Computational Biology BMC Bioinformatics, vol. 8(Suppl. 10). Supplementary pages http://www.biomedcentral.com/1471-2105/8/S10/S1

Sonnenburg, S, Rätsch, G, and Rieck, K (2007). Large Scale Learning with String Kernels In: Large-Scale Kernel Machines, ed. by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston. MIT Press, Cambridge, MA, chap. 4, pp. 73-104. Printable file

Sonnenburg, S, Braun, ML, Ong, CS, Bengio, S, Bottou, L, Holmes, G, LeCun, Y, Müller, K, Pereira, F, Rasmussen, CE, Rätsch, G, Schölkopf, B, Smola, A, Vincent, P, Weston, J, and Williamson, RC (2007). The Need for Open Source Software in Machine Learning Journal of Machine Learning Research, 8:2443-2466. Supplementary pages http://jmlr.csail.mit.edu/papers/v8/sonnenburg07a.html Printable file

2006

Warmuth, MK, Liao, J, and Rätsch, G (2006). Totally Corrective Boosting Algorithms that Maximize the Margin In: Proceedings of the International Conference on Machine Learning, ed. by William Cohen and Andrew Moore, pp. 1001-1008, Pittsburg. IMLS. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/Members/raetsch/papers/totalboost\_icml06.pdf

Rätsch, G and Sonnenburg, S (2006). Learning interpretable SVMs for biological sequence classification BMC Bioinformatics, 7(Suppl.1):S9. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/mkl_splice Printable file

Sonnenburg, S, Zien, A, and Rätsch, G (2006). ARTS: Accurate Recognition of Transcription Starts in Human Bioinformatics, 22(14):e472-e480. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/arts/

Sonnenburg, S, Rätsch, G, and Schäfer, C (2006). A general and efficient multiple kernel learning algorithm In: Advances in Neural Information Processing Systems (NIPS'08), ed. by Y. Weiss and B. Schölkopf and J. Platt, vol. 15, pp. 1273-1280, Cambridge, MA, MIT Press. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/mkl_silp/

Sonnenburg, S, Rätsch, G, Schäfer, C, and Schölkopf, B (2006). Large scale multiple kernel learning Journal of Machine Learning Research, 7:1531-1565. Printable file

Chechik, G, Leslie, C, Rätsch, G, and Tsuda, K (2006). New Problems and Methods in Computational Biology BMC Bioinformatics, vol. 7(Suppl. 1). Supplementary pages http://www.biomedcentral.com/bmcbioinformatics/7?issue=S1

Rätsch, G, Hepp, B, Schulze, U, and Ong, CS (2006). PALMA: Perfect Alignments using Large Margin Algorithms In: German Conference on Bioinformatics, pp. 104-113, Berlin,Heidelberg, Springer Verlag. LNCS. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/palma/palma_gcb Printable file

Zien, A, Ong, CS, and Rätsch, G (2006). Towards the Inference of Graphs on Ordered Vertices Max Planck Institute for biological Cybernetics, Research Note(150), Tübingen, Germany. Supplementary pages http://www.kyb.mpg.de/publication.html?publ=4133 Printable file

Shin, H, Hill, NJ, and Raetsch, G (2006). Graph-based Semi-Supervised Learning with Sharper Edges Lecture Notes in Artificial Intelligence , 4212:402-413.

2005	Rätsch, G, Sonnenburg, S, and Schölkopf, B (2005). RASE: Recognition of alternatively Spliced Exons in C. elegans In: Bioinformatics, vol. 21(Suppl. 1), pp. i369. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/RASE

Sonnenburg, S, Rätsch, G, and Schäfer, C (2005). Learning interpretable SVMs for biological sequence classification In: RECOMB 2005, LNBI 3500, pp. 389-407, Berlin Heidelberg, Springer-Verlag. Printable file

Tsuda, K, Rätsch, G, and Warmuth, MK (2005). Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection Journal of Machine Learning Research, 6:995-1018. Supplementary pages http://www.jmlr.org/papers/volume6/tsuda05a/tsuda05a.pdf

Sonnenburg, S, Rätsch, G, and Schölkopf, B (2005). Large Scale Genomic Sequence SVM Classifiers In: Proceedings of the International Conference on Machine Learning, ICML. Supplementary pages http://www.tuebingen.mpg.de/raetsch/projects/LargeScaleSVMs\_ICML2005

Rätsch, G and Warmuth, M (2005). Efficient Margin Maximization with Boosting Journal of Machine Learning Research, 6:2131-2152. Supplementary pages http://www.jmlr.org/papers/volume6/ratsch05a/ratsch05a.pdf

Müller, K, Rätsch, G, Sonnenburg, S, Mika, S, Grimm, M, and Heinrich, N (2005). Classifying 'Drug-likeness' with Kernel-Based Learning Methods J. Chem. Inf. Model, 45:249-253.

Tsuda, K and Rätsch, G (2005). Image reconstruction by linear programming IEEE Transactions on Image Processing, 14(6):737-744. Supplementary pages http://dx.doi.org/10.1109/TIP.2005.846029 Printable file

2004

Rätsch, G and Sonnenburg, S (2004). Accurate Splice Site Detection for Caenorhabditis elegans In: Kernel Methods in Computational Biology, ed. by B. Schölkopf, K. Tsuda and J.-P. Vert, pp. 277-298, MIT Press. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/MITBookSplice/files/RaeSon04.pdf

Knabe, S, Mika, S, Müller, K, Rätsch, G, and Schruff, W (2004). Zur Beurteilung des Fraud-Risikos im Rahmen der Abschlussprüfung Die Wirtschaftsprüfung, 19(04):1058-1068.

2003	Tsuda, K and Rätsch, G (2003). Image Reconstruction by Linear Programming Max-Planck Institute for Biological Cybernetics, Tübingen. Supplementary pages documents/TsuRae03.pdf

Rätsch, G (2003). Robust Multi-Class Boosting In: EuroSpeech, pp. 997-1000, IEEE, Geneva. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/Members/raetsch/papers/Rae03.pdf

Mika, S, Rätsch, G, Weston, J, Schölkopf, B, Smola, A, and Müller, K (2003). Constructing Descriptive and Discriminative Non-Linear Features: Rayleigh Coefficients in Kernel Feature Space IEEE PAMI (http://www.computer.org/tpami, 25(5):623-633. Supplementary pages http://csdl.computer.org/comp/trans/tp/2003/05/i0623abs.htm,

Rätsch, G, Smola, AJ, and Mika, S (2003). Adapting Codes and Embeddings for Polychotomies In: Advances in Neural information processing systems (http://www-2.cs.cmu.edu/Web/Groups/NIPS/NIPS2002/nips-papers.html), MIT Press.

Warmuth, MK, Liao, J, Rätsch, G, Mathieson, M, Putta, S, and Lemmen, C (2003). Active Learning with SVMs in the Drug Discovery Process Chemical Information and Computer Sciences (http://pubs.acs.org/journals/jcisd8), 43(2):667-673. Supplementary pages http://dx.doi.org/10.1021/ci025620t,

Meir, R and Rätsch, G (2003). An Introduction to Boosting and Leveraging In: Advanced Lectures on Machine Learning, ed. by S. Mendelson and A. Smola, vol. 2006, pp. 118-183, Springer Verlag. Lecture Notes in Computer Science. Supplementary pages http://www.springerlink.com/content/8574x0tm63nvjbem Printable file

2002	Rätsch, G (2002). Robustes Boosting durch konvexe Optimierung In: Ausgezeichnete Informatikdissertationen 2001, ed. by D. Wagner et al., pp. 125-136, Bonner Köllen. Supplementary pages http://www2.fml.tuebingen.mpg.de/raetsch/Members/raetsch/papers/Rae02.pdf

Rätsch, G and Warmuth, MK (2002). Marginal Boosting In: Proceedings of the Annual Conferences on Computational Learning Theory.

Sonnenburg, S, Rätsch, G, Jagota, A, and Müller, KR (2002). New Methods for Splice Site Recognition In: ICANN '02: Proceedings of the International Conference on Artificial Neural Networks, pp. 329 - 336, Springer-Verlag. Supplementary pages http://www.fml.tuebingen.mpg.de/raetsch/projects/AnuSplice Printable file

Tsuda, K, Kawanabe, M, Rätsch, G, Sonnenburg, S, and Müller, KR (2002). A New Discriminative Kernel from Probabilistic Models Neural Computation, 14:2397-2414. Supplementary pages http://www.mitpressjournals.org/doi/abs/10.1162/08997660260293274

Rätsch, G, Mika, S, and Warmuth, MK (2002). On the Convergence of Leveraging In: Advances in Neural information processing systems , ed. by T. G. Dietterich and S. Becker and Z. Ghahramani.

Tsuda, K, Kawanabe, M, Rätsch, G, Sonnenburg, S, and Müller, KR (2002). A New Discriminative Kernel from Probabilistic Models In: Advances in Neural information processing systems , ed. by T. G. Dietterich and S. Becker and Z. Ghahramani.

Warmuth, MK, Rätsch, G, Mathieson, M, Liao, J, and Lemmen, C (2002). Active Learning in the Drug Discovery Process In: Advances in Neural information processing systems (http://www-2.cs.cmu.edu/Web/Groups/NIPS/NIPS2001/nips.html), ed. by T. G. Dietterich and S. Becker and Z. Ghahramani.

Rätsch, G, Mika, S, Schölkopf, B, and Müller, K (2002). Constructing Boosting Algorithms from SVMs: an Application to One-Class Classification IEEE TPAMI, 24(9).

Rätsch, G, Demiriz, A, and Bennett, K (2002). Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces Machine Learning, 48(1-3):189 - 218 . Supplementary pages http://www.springerlink.com/content/augw7rrp8vhcjgvv/ Printable file

2001	Rätsch, G (2001). Robust Boosting via Convex Optimization PhD thesis, University of Potsdam, Mathematisch-Naturwissenschaftliche Fakultät, Universität Potsdam. Printable file

Mika, S, Rätsch, G, and Müller, K (2001). A mathematical programming approach to the Kernel Fisher algorithm In: Proc. NIPS 13 (http://www.cs.cmu.edu/afs/cs/project/cnbc/nips/NIPS.html), ed. by T. K. Leen and T. G. Dietterich and V. Tresp, pp. 591-597, MIT Press.

Müller, K, Mika, S, Rätsch, G, Tsuda, K, and Schölkopf, B (2001). An Introduction to Kernel-based Learning Algorithms IEEE Neural Networks, 12(2):181-201. Supplementary pages http://mlg.anu.edu.au/˜raetsch/ps/review.ps.gz

Onoda, T, Rätsch, G, and Müller, KR (2001). An Arcing algorithm with an intuitive learning control parameter Journal of the Japanese Society fo AI (http://www.jssst.or.jp/jsai/journal/index/index-e.html), 16(5C):417-426. Supplementary pages https://olj.nii.ac.jp:443/cgi-service/journal.cgi?FN=202&LANG=c&GID=8&JID=JSAITR&JDID=01062506

Rätsch, G, Mika, S, and Warmuth, MK (2001). On the Convergence of Leveraging Royal Holloway College, NeuroCOLT2, Technical Report (98), London. Supplementary pages http://www2.boosting.org/boosting/papers/upload\_18385\_EnsembleConvergence\_web.ps.gz

Rätsch, G, Onoda, T, and Müller, K (2001). Soft Margins for AdaBoost Machine Learning, 42(3):287-320. Printable file

Rätsch, G and Warmuth, MK (2001). Marginal Boosting Royal Holloway College, NeuroCOLT2, Technical Report (97), London. Supplementary pages http://www2.boosting.org/boosting/papers/upload\_18385\_MaxMargin\_paper2\_web.ps.gz

Tsuda, K, Rätsch, G, Mika, S, and Müller, K (2001). Learning To Predict the Leave-one-out Error of Kernel based classifiers In: Proc. ICANN'01.

2000	Rätsch, G, Schölkopf, B, Mika, S, and Müller, K (2000). SVM and Boosting: One Class GMD FIRST, Technical Report (119), Berlin.

Rätsch, G, Demiriz, A, and Bennett, K (2000). Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces Royal Holloway College, NeuroCOLT2, Technical Report , London.

Rätsch, G, Warmuth, M, Mika, S, Onoda, T, Lemm, S, and Müller, K (2000). Barrier Boosting In: Proc. COLT'00, ed. by Morgan Kaufmann, pp. 170-179, Palo Alto.

Kohlmorgen, J, Lemm, S, Rätsch, G, and Müller, K (2000). Analysis of Nonstationary Time Series by Mixtures of Self-Organizing Predictors In: Proc.NNSP'2000 (http://eivind.imm.dtu.dk/nnsp2000/), pp. 85-94, Sydney.

Mika, S, Rätsch, G, Weston, J, Schölkopf, B, Smola, AJ, and Müller, K (2000). Learning Discriminative and Invariant Nonlinear Features Unpublished.

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

Onoda, T, Rätsch, G, and Müller, K (2000). A Non-Intrusive Monitoring System for Household Electric Appliances with Inverters In: Proc. of NC'2000, Berlin.

Onoda, T and Rätsch, G (2000). Trends in Boosting Research and Applications Central Research Institute of the Electric Power Industry, CRIEPI, Tokyo.

Rätsch, G, Schölkopf, B, Smola, AJ, Mika, S, Onoda, T, and Müller, K (2000). Robust Ensemble Learning In: Proc. of the NIPS*Workshop on Large Margin Classifiers: Advances in Large Margin Classifiers, ed. by A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, pp. 207-219, MIT Press, Cambridge, MA.

Rätsch, G, Schölkopf, B, Smola, AJ, Mika, S, Onoda, T, and Müller, K (2000). Robust Ensemble Learning for Data Mining In: Proc. of PAKDD'2000. Lecture Notes in Artificial Intelligence, Springer-Verlag.

Rätsch, i, Schölkopf, B, Smola, AJ, Müller, K, Onoda, T, and Mika, S (2000). $\nu $-Arc: Ensemble Learning in the Presence of Outliers In: Proc. NIPS 12 , ed. by S. A. Solla and T. K. Leen and K.-R. Müller, pp. 561-567, MIT Press.

Mika, S, Rätsch, G, Weston, J, Schölkopf, B, Smola, AJ, and Müller, K (2000). Invariant Feature Extraction and Classification in Kernel Spaces In: Proc. NIPS 12 (http://www.cs.cmu.edu//Web/Groups/NIPS/NIPS99/nips99.html), ed. by S. A. Solla and T. K. Leen and K.-R. Müller, pp. 526-532, MIT Press.

Zien, A, Rätsch, G, Mika, S, Schölkopf, B, Lengauer, T, and Müller, K (2000). Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA Bioinformatics, 16(9):799-807.

1999	Mika, S, Rätsch, G, Weston, J, Schölkopf, B, and Müller, K (1999). Fisher Discriminant Analysis with Kernels In: Proc. NNSP'99, ed. by Y. - H. Hu, J. Larsen, E. Wilson and S. Douglas, pp. 41-48, IEEE.

Smola, A, Schölkopf, B, and Rätsch, G (1999). Linear Programs for Automatic Accuracy Control in Regression In: Proc. ICANN'99, Berlin, Springer-Verlag.

Schölkopf, B, Mika, S, Burges, CJ, Knirsch, P, Müller, K, Rätsch, G, and Smola, AJ (1999). Input Space vs. Feature Space in Kernel-Based Methods IEEE Transanctions on Neural Networks, 10(5):1000-1017.

Zien, A, Rätsch, G, Mika, S, Schölkopf, CL, Smola, AJ, Lengauer, T, and Mueller, K (1999). Engineering Support Vector Machine Kernel That Recognize Translation Initiation Sites in DNA In: Proceedings GCB'99.

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

Rätsch, G, Onoda, T, and Müller, K (1999). Regularizing AdaBoost In: Proc. NIPS 11, ed. by M. S. Kearns, S. A. Solla and D. A. Cohn, pp. 564-570, MIT Press.

Mika, S, Schölkopf, B, Smola, AJ, Müller, K, Scholz, M, and Rätsch, G (1999). Kernel PCA and De-Noising in Feature Spaces In: Proc. NIPS 11, ed. by M. S. Kearns, S. A. Solla and D. A. Cohn, pp. 536-542, MIT Press.

1998	Rätsch, G, Onoda, T, and Müller, K (1998). Soft Margins for AdaBoost Royal Holloway College, NeuroCOLT, Technical Report(NC-TR-1998-021), University of London.

Rätsch, G, Onoda, T, and Müller, K (1998). An improvement of AdaBoost to avoid overfitting In: Proc. ICONIP, pp. 506-509, Kitakyushu, Japan.

Rätsch, G (1998). Ensemble Learning Methods for Classification Diplom thesis, University of Potsdam, Neues Palais 10, Potsdam. Printable file

Müller, K, Smola, A, Rätsch, G, Schölkopf, B, Kohlmorgen, J, and Vapnik, V (1998). Using Support Vector Machines for Time Series Prediction In: Advances in Kernel Methods - Support Vector Learning, Proc. of the NIPS Workshop on Support Vectors, ed. by B. Schölkopf, C. Burges and A. Smola, MIT Press, Cambridge, MA.

Onoda, T, Rätsch, G, and Müller, K (1998). An asymptotic analysis of AdaBoost in the binary classification case In: Proc. ICANN'98, ed. by L. Niklasson, M.Bodén and T. Ziemke, pp. 195-200.

Schölkopf, B, Mika, S, Smola, AJ, Rätsch, G, and Müller, K (1998). Kernel PCA Pattern Reconstruction via Approximate Pre-Images In: Proceedings of the 8th International Conference on Artificial Neural Networks, ed. by L. Niklasson, M.Bodén and T. Ziemke, pp. 147-152, Berlin, Springer Verlag.

1997	Müller, K, Smola, A, Rätsch, G, Schölkopf, B, Kohlmorgen, J, and Vapnik, V (1997). Using Support Vector Machines for Time Series Prediction In: Proc. ICANN'97, ed. by W. Gerstner, A. Germond, M. Hasler and J. - D. Nicoud, pp. 999-1004, Berlin, Springer Verlag.