Publikationen von S Sonnenburg

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

Schweikert, G.; Zien, A.; Zeller, G.; Behr, J.; Dieterich, C.; Ong, C.; Philips, P.; De Bona, F.; Hartmann, L.; Bohlen, A. et al.; Krüger, N.; Sonnenburg, S.; Rätsch, G.: mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research 19 (11), S. 2133 - 2143 (2009)

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

Schweikert, G.; Behr, J.; Zien, A.; Zeller, G.; Ong, C.; Sonnenburg, S.; Rätsch, G.: mGene.web: a web service for accurate computational gene finding. Nucleic Acids Research (London) 37 (Supplement 2), S. W312 - W316 (2009)

Ben-Hur, A.; Ong, C.; Sonnenburg, S.; Schölkopf, B.; Rätsch, G.: Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology 4 (10), e1000173 (2008)

Sonnenburg, S.; Braun, M.; Ong, C.; Bengio, S.; Bottou, L.; Holmes , G.; LeCun, Y.; Müller, K.-R.; Pereira, F.; Rasmussen, C. et al.; Rätsch, G.; Schölkopf, B.; Smola, A.; Vincent , P.; Weston, J.; Williamson, R.: The Need for Open Source Software in Machine Learning. The Journal of Machine Learning Research 8, S. 2443 - 2466 (2007)

Rätsch, G.; Sonnenburg, S.; Srinivasan , J.; Witte, H.; Müller, K.-R.; Sommer, R.; Schölkopf, B.: Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Computational Biology 3 (2 ), e20, S. 0313 - 0322 (2007)

Sonnenburg, S.; Rätsch, G.; Schäfer, C.; Schölkopf, B.: Large Scale Multiple Kernel Learning. The Journal of Machine Learning Research 7, S. 1531 - 1565 (2006)

Müller, K.-R.; Rätsch, G.; Sonnenburg, S.; Mika, S.; Grimm, M.; Heinrich, N.: Classifying "drug-likeness" with kernel-based learning methods. Journal of Chemical Information and Modeling 45 (2), S. 249 - 253 (2005)

Tsuda, K.; Kawanabe, M.; Rätsch, G.; Sonnenburg, S.; Müller, K.: A New Discriminative Kernel from Probabilistic Models. Neural computation 14 (10), S. 2397 - 2414 (2002)

Sonnenburg, S.: Maschinelles Lernen zur Genom-Sequenzanalyse. In: Ausgezeichnete Informatikdissertationen 2008, S. 281 - 290 (Hg. Wagner, D.; Bernstein, A.). Gesellschaft für Informatik, Bonn, Germany (2009)

Sonnenburg, S.; Rieck, K.; Rätsch, G.: Large Scale Learning with String Kernels. In: Large-scale kernel machines, 4, S. 73 - 103 (Hg. Bottou, L.). MIT Press (2007)

Rätsch, G.; Sonnenburg, S.: Accurate Splice Site Detection for Caenorhabditis elegans. In: Kernel Methods in Computational Biology, S. 277 - 298 (Hg. Schölkopf, B.; Tsuda, K.; Vert, J.-P.). MIT Press, Cambridge, MA, USA (2004)

Goernitz, N.; Widmer, C.; Zeller, G.; Kahles, A.; Rätsch, G.; Sonnenburg, S.: Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. In: Advances in Neural Information Processing Systems 24, S. 2690 - 2698 (Hg. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira , F.; Weinberger, K.). 25th Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain, 12. Dezember 2011 - 14. Dezember 2011. Curran, Red Hook, NY, USA (2012)

Jenssen, R.; Kloft, M.; Sonnenburg, S.; Zien, A.; Müller, K.-R.: A new scatter-based multi-class support vector machine. In: 2011 IEEE International Workshop on Machine Learning for Signal Processing (Hg. Tan, T.). IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011), Beijing, China, 18. September 2011 - 21. September 2011. IEEE, Piscataway, NJ, USA (2011)

Sonnenburg, S.; Franc, V.: COFFIN: A Computational Framework for Linear SVMs. In: 27th International Conference on Machine Learning (ICML 2010), 280, S. 999 - 1006 (Hg. Langford, J.; Pineau, J.). 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 21. Juni 2010 - 24. Juni 2010. International Machine Learning Society, Madison, WI, USA (2010)

Kloft, M.; Brefeld, U.; Sonnenburg, S.; Laskov, P.; Müller, K.-R.; Zien, A.: Efficient and Accurate Lp-Norm Multiple Kernel Learning. In: Advances in Neural Information Processing Systems 22, S. 997 - 1005 (Hg. Bengio, Y.; Schuurmans, D.; Lafferty, J.; Williams, C.; Culotta, A.). 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Vancouver, BC, Canada, 07. Dezember 2009 - 10. Dezember 2009. Curran, Red Hook, NY, USA (2010)

Zien, A.; Krämer, N.; Sonnenburg, S.; Rätsch, G.: The Feature Importance Ranking Measure. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II, S. 694 - 709 (Hg. Buntine, W.; Grobelnik, M.; Mladenić, D.; Shawe-Taylor, J.). 16th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009), Bled, Slovenia, 07. September 2009 - 11. September 2009. Springer, Berlin, Germany (2009)

Sonnenburg, S.; Zien, A.; Philips, P.; Rätsch, G.: POIMs: positional oligomer importance matrices: understanding support vector machine-based signal detectors. 16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB 2008), Toronto, Canada, 19. Juli 2008 - 23. Juli 2008. Bioinformatics 24 (13), S. i6 - i14 (2008)

Sonnenburg, S.; Schweikert, G.; Philips, P.; Behr, J.; Rätsch, G.: Accurate Splice site Prediction Using Support Vector Machines. NIPS 2006 Workshop on New Problems and Methods in Computational Biology, Whistler, Canada, 08. Dezember 2006. BMC Bioinformatics 8 (Supplement 10), S7, (2007)