Publications of Gunnar Rätsch

2011

Schultheiss, SJ, Jean, G, Behr, J, Drewe, P, Görnitz, N, Kahles, A, Mudrakarta, P, Sreedharan, VT, Zeller, G, and Rätsch, G (2011). oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS data In: 7th ISCB Student Council Symposium, ed. by Priscilla Grynberg, vol. 12(Suppl 11), pp. A7, ISCBSC, Vienna, Austria, BMC Bioinformatics.

Eichner, J, Zeller, G, Laubinger, S, and Rätsch, G (2011). Detection of Alternative Splicing in Arabidopsis thaliana from Whole-Genome Tiling Arrays with Support Vector Machines BMC Bioinformatics, 12(55). Supplementary material

Rösel, T, Hung, L, Medenbach, J, Donde, K, Benes, V, Rätsch, G, and Bindereif, A (2011). RNA-Seq analysis in mutant zebrafish reveals role of U1C protein in alternative splicing regulation EMBO Journal, 30:1965 - 1976.

Schultheiss, SJ, Münch, M, Andreeva, GD, and Rätsch, G (2011). Persistence and Availability of Web Services in Computational Biology PLoS ONE, 6(9):e24914.

Görnitz, N, Widmer, C, Zeller, G, Kahles, A, Sonnenburg, S, and Rätsch, G (2011). Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation In: Advances in Neural Information Processing Systems (NIPS'11), NIPS Foundation.

Widmer, C and Rätsch, G (2011). Transfer Learning in Computational Biology In: Proceedings on the Workshop on Unsupervised and Transfer Learning, ed. by Isabelle Guyon and Danny Silver, JMLR.

Gan, X, Stegle, O, Behr, J, Steffen, JG, Drewe, P, Hildebrand, KL, Lyngsoe, R, Schultheiss, SJ, Osborne, EJ, Sreedharan, VT, Kahles, A, Bohnert, R, Jean, G, Derwent, P, Kersey, P, Belfield, EJ, Harberd, NP, Kemen, E, Toomajian, C, Kover, PX, Clark, RM, Ratsch, G, and Mott, R (2011). Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature. Printable file Supplementary website

2010	Widmer, C, Leiva, J, Altun, Y, and Rätsch, G (2010). Leveraging Sequence Classification by Taxonomy-based Multitask Learning In: Proc. RECOMB'10. Printable file

Stegle, O, Drewe, P, Bohnert, R, Borgwardt, K, and Rätsch, G (2010). Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts Preprint. Printable file Supplementary material Supplementary website

Sonnenburg, S, Rätsch, G, Henschel, S, Widmer, C, Behr, J, Zien, A, de Bona, F, Binder, A, Gehl, C, and Franc, V (2010). The SHOGUN Machine Learning Toolbox Journal of Machine Learning Research, 11:1799-1802.

Rätsch, G and Bohnert, R (2010). Modern Methods for Transcriptome Reconstruction In: Jahresbericht der Max-Planck-Gesellschaft, Max-Planck-Gesellschaft, München. Printable file Supplementary material

Bohnert, R and Rätsch, G (2010). rQuant.web: a tool for RNA-Seq-based transcript quantitation Nucleic Acids Research, 38(Suppl 2):W348-51. Printable file Supplementary material Supplementary website

Widmer, C, Toussaint, N, Altun, Y, Kohlbacher, O, and Rätsch, G (2010). Novel Machine Learning Methods for MHC Class I Binding Prediction In: Proc. Pattern Recognition in Bioinformatics. Springer.

Toussaint, NC, Widmer, C, Kohlbacher, O, and Rätsch, G (2010). Exploiting Physico-Chemical Properties in String Kernels BMC Bioinformatics, 11(Suppl. 8):S7. Printable file Supplementary material

Widmer, C, Toussaint, N, Altun, Y, and Rätsch, G (2010). Inferring Latent Task Structure for Multi-Task Learning by Multiple Kernel Learning BMC Bioinformatics, 11(Suppl. 8):S5. Printable file Supplementary material

Laubinger, S, Zeller, G, Henz, SR, Buechel, S, Sachsenberg, T, Wang, J, Rätsch, G, and Weigel, D (2010). Global effects of the small RNA biogenesis machinery on the Arabidopsis thaliana transcriptome PNAS. Printable file Supplementary material

Jean, G, Kahles, A, Sreedharan, VT, De Bona, F, and Rätsch, G (2010). RNA-Seq Read Alignments with PALMapper Curr. Protoc. Bioinform., 32:11.6.1- 11.6.37. Printable file Supplementary website

Behr, J, Bohnert, R, Zeller, G, Schweikert, G, Hartmann, L, and Rätsch, G (2010). Next generation genome annotation with mGene.ngs BMC Bioinformatics, 11(S10):O8. Supplementary material

Hallem, EA, Spencer, WC, McWhirter, RD, Zeller, G, Henz, SR, Rätsch, G, Miller III, DM, Horvitz, HR, Sternberg, PW, and Ringstad, N (2010). A receptor-type guanylate cyclase is required for carbon dioxide sensation by C. elegans PNAS. Printable file Supplementary material

Gerstein, MB, et al.,, Henz, SR, et al.,, Rätsch, G, et al.,, Zeller, G, et al.,, and Waterston, RH (2010). Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project Science, 330(6012):1775-1787. Printable file

Spencer, WC, Zeller, G, Watson, JD, Henz, SR, Watkins, KL, McWhirter, RD, Petersen, S, Sreedharan, VT, Widmer, C, Reinke, V, Petrella, L, Strome, S, Von Stetina, S, Katz, M, Rätsch, G, and Miller III, DM (2010). A Spatial and Temporal Map of C. elegans Gene Expression Genome Research. Printable file Supplementary website

2009	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, 37(Web Server issue):W312–W316. Printable file Supplementary material

Zien, A, Krämer, N, Sonnenburg, S, and Rätsch, G (2009). The Feature Importance Ranking Measure In: Proc. ECML PKDD, ed. by W. Buntine, M. Grobelnik, D. Mladenic, J. Shawe-Taylor, vol. 5782/2009, pp. 694-709, Springer Berlin / Heidelberg, Springer, XXIX ed.. 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, 19:2133-2143. Printable file Supplementary material Supplementary website

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. Printable file Supplementary material

Schultheiss, SJ, Busch, W, Lohmann, JU, Kohlbacher, O, and Rätsch, G (2009). KIRMES: Kernel-based identification of regulatory modules in euchromatic sequences Bioinformatics, 25(16):2126-2133. Printable file Supplementary material

Schultheiss, SJ, Busch, W, Lohmann, JU, Kohlbacher, O, and Rätsch, G (2009). KIRMES: kernel-based identification of regulatory modules in euchromatic sequences BMC Bioinformatics, 10(Suppl 13):O1. Printable file Supplementary material

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, 106(30):12273-8. Printable file Supplementary material Supplementary website

Bohnert, R, Behr, J, and Rätsch, G (2009). Transcript quantification with RNA-Seq data BMC Bioinformatics, 10(S13):P5. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

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. Printable file Supplementary material

2007	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. Printable file Supplementary material

Schulze, U, Hepp, B, Ong, CS, and Rätsch, G (2007). PALMA: mRNA to Genome Alignments using Large Margin Algorithms Bioinformatics, 23(15):1892-1900. Printable file Supplementary material

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. Printable file Supplementary material Supplementary website

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 material

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

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. Printable file Supplementary material

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. Printable file Supplementary material

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 material

Rätsch, G and Sonnenburg, S (2006). Learning interpretable SVMs for biological sequence classification BMC Bioinformatics, 7(Suppl.1):S9. Printable file Supplementary material

Sonnenburg, S, Zien, A, and Rätsch, G (2006). ARTS: Accurate Recognition of Transcription Starts in Human Bioinformatics, 22(14):e472-e480. Supplementary material

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 material

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 material

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. Printable file Supplementary material

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. Printable file Supplementary material

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 material

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 material

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 material

Rätsch, G and Warmuth, M (2005). Efficient Margin Maximization with Boosting Journal of Machine Learning Research, 6:2131-2152. Supplementary material

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. Supplementary material

Tsuda, K and Rätsch, G (2005). Image reconstruction by linear programming IEEE Transactions on Image Processing, 14(6):737-744. Printable file Supplementary material

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 material

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 material

Rätsch, G (2003). Robust Multi-Class Boosting In: EuroSpeech, pp. 997-1000, IEEE, Geneva. Supplementary material

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 material

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 material

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. Printable file Supplementary material

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 material

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. Printable file Supplementary material

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 material

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 . Printable file Supplementary material

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 material

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 material

Rätsch, G, Mika, S, and Warmuth, MK (2001). On the Convergence of Leveraging Royal Holloway College, NeuroCOLT2, Technical Report (98), London. Supplementary material

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 material

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.