Publications of G Rätsch

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, pp. 1799 - 1802 (2010)

Stegle, O.; Drewe, P.; Bohnert, R.; Borgwardt, K.; Rätsch, G.: Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts. Nature Precedings 2010, pp. 1 - 11 (2010)

Schweikert, G.; Zeller, G.; Behr, J.; Dieterich, C.; Ong, C.; Philips, P.; De Bona, F.; Hartmann, L.; Bohlen, A.; Krüger, N. et al.; Sonnenburg, S.; Rätsch, G.: mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research 19 (11), pp. 2133 - 2143 (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), pp. W312 - W316 (2009)

Graf, A.; Bousquet, O.; Rätsch, G.; Schölkopf, B.: Prototype Classification: Insights from Machine Learning. Neural computation 21 (1), pp. 272 - 300 (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)

Laubinger, S.; Zeller, G.; Henz, S.; Sachsenberg, T.; Widmer, C.; Naouar, N.; Vuylsteke, M.; Schölkopf, B.; Rätsch, G.; Weigel, D.: At-TAX: A Whole Genome Tiling Array Resource for Developmental Expression Analysis and Transcript Identification in Arabidopsis thaliana. Genome Biology 9 (7), R112, pp. 1 - 16 (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, pp. 2443 - 2466 (2007)

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

Schulze, U.; Hepp, B.; Ong, C.; Rätsch, G.: PALMA: mRNA to Genome Alignments using Large Margin Algorithms. Bioinformatics 23 (15), pp. 1892 - 1900 (2007)

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

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

Tsuda, K.; Rätsch, G.: Image Reconstruction by Linear Programming. IEEE Transactions on Image Processing 14 (6), pp. 737 - 744 (2005)

Tsuda, K.; Rätsch, G.; Warmuth , M.: Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection. The Journal of Machine Learning Research 6, pp. 995 - 1018 (2005)

Mika, S.; Rätsch, G.; Weston, J.; Schölkopf, B.; Smola, A.; Müller, K.-R.: Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), pp. 623 - 628 (2003)

Rätsch, G.; Mika, S.; Schölkopf, B.; Müller, K.-R.: Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), pp. 1184 - 1199 (2002)

Schölkopf, B.; Mika, S.; Burges, C.; Knirsch, P.; Müller, K.-R.; Rätsch, G.; Smola, A.: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10 (5), pp. 1000 - 1017 (1999)

Müller, K.; Mika, S.; Rätsch, G.; Tsuda, K.; Schölkopf, B.: An Introduction to Kernel-Based Learning Algorithms. In: Handbook of neural network signal processing: Neural network signal processing, 4, pp. 95 - 134 (Eds. Hu, Y.; Hwang, J.-N.; Perry, S.). CRC Press, Boca Raton, FL, USA (2002)

Chechik, G.; Leslie, C.; Noble, W.; Rätsch, G.; Morris, Q.; Tsuda, K. (Eds.): NIPS workshop on New Problems and Methods in Computational Biology (BMC Bioinformatics, 8). NIPS Workshop on New Problems and Methods in Computational Biology 2006, Whistler, Canada, December 08, 2006. (2007)

Machine Learning in Computational Biology. NIPS 2007 Workshop: Machine Learning in Computational Biology (MLCB 2007), Whistler, Canada, December 07, 2007 - December 08, 2007. (2007)