Publications of Sören Sonnenburg

2011	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. Printable file

2010	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. Printable file

Sonnenburg, S and Franc, V (2010). COFFIN : A Computational Framework for Linear SVMs In: Proc. ICML 2010. Supplementary website Printable file

Kloft, M, Brefeld, U, Sonnenburg, S, and Zien, A (2010). Non-Sparse Regularization and Efficient Training with Multiple Kernels University of California, Berkeley. Printable file

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. Online access Printable file

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. Online access Supplementary website Printable file

Franc, V and Sonnenburg, S (2009). Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization Journal of Machine Learning Research, 10:2157--2192. Printable file

Kloft, M, Brefeld, U, Sonnenburg, S, Laskow, P, Mueller, K, and Zien, A (2009). Efficient and Accurate Lp-Norm MKL In: Advances in Neural Information Processing Systems 21, ed. by Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta, pp. 997--1005, Cambridge, MA, MIT Press.

Jenssen, R, Kloft, M, Zien, A, Sonnenburg, S, and Müller, K (2009). A Multi-Class Support Vector Machine Based on Scatter Criteria Technical University Berlin, Technical Report(014-2009). Online access Printable file

2008	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. Online access 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. Online access Printable file

Sonnenburg, S (2008). Machine Learning for Genomic Sequence Analysis PhD thesis, Berlin Institute of Technology. Printable file

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. Online access 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. Online access Supplementary website Printable file

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. Online access 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. Online access Printable file

Zien, A, Sonnenburg, S, and Philips, P (2007). Computing Positional Oligomer Importance Matrices (POIMs) Fraunhofer Institute FIRST, technical report(2). Online access Printable file

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

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

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. Online access

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

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. Online access

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

Sonnenburg, S, Rätsch, G, and Schölkopf, B (2005). Large Scale Genomic Sequence SVM Classifiers In: Proceedings of the International Conference on Machine Learning, ICML. Online access

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. Online access

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. Online access

2002	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. Online access 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. Online access

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.